Abstract
Preventing/mitigating natural disasters in urban areas can indirectly be part of the 17 sustainable economic and social development intentions according to the United Nations in 2015. Four types of natural disasters—flooding, heavy rain-induced slope failures/landslides; earthquakes causing structure failure/collapse, and land subsidence—are briefly considered in this article. With the increased frequency of climate change-induced extreme weathers, the numbers of flooding and heavy rain-induced slope failures/landslides in urban areas has increased in recent years. There are both engineering methods to prevent their occurrence, and more effectively early prediction and warning systems to mitigate the resulting damage. However, earthquakes still cannot be predicted to an extent that is sufficient to avoid damage, and developing and adopting structures that are resilient against earthquakes, that is, structures featuring earthquake resistance, vibration damping, and seismic isolation, are essential tasks for sustainable city development. Land subsidence results from human activity, and is mainly due to excessive pumping of groundwater, which is a “natural” disaster caused by human activity. Countermeasures include effective regional and/or national freshwater management and local water recycling to avoid excessive pumping the groundwater. Finally, perspectives for risk warning and hazard prevention through enhanced field monitoring, risk assessment with multi-criteria decision-making (MCDM), and artificial intelligence (AI) technology.
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1 Introduction
Owing to convenient living environment in urban areas, an increasing number of people worldwide are choosing to live in these areas; that is, worldwide urbanization is occurring. Cohen [1] reported that almost half of humanity now resides in urban areas. Developing countries experience faster growth of this kind. In China, from 1980 to 2021, the ratio of people living in cities to the total population increased from 19.37% to 64.72% [2]. An increase in cities population results in the expansion of existing cities or the creation of new ones. For example, Shanghai, China has been developed/expanded to an urban megapolis with over 25 million inhabitants.
Although urban areas are convenient and comfortable for people, they are vulnerable for natural disasters, including floods, earthquakes, and landslides. In densely populated urban areas, natural disasters can cause serious property damage and death. For example, the Great Hanshin-Awaji Earthquake that occurred in the Osaka metropolitan area, Japan, on 17 January 1995, with a magnitude of 7.3 caused 6,434 casualties [3]. Furthermore, global warming is one of the major environmental problems worldwide. One of the consequences of global warming is the frequent occurrence of extreme weather [4], such as torrential rains or “a belt zone of heavy precipitation” [5], which can cause water-related disasters in urban areas. According to the United Nations (September 2021), extreme weather-induced natural hazards have increased fivefold within the last 50 years. To maintain the sustainable development of cities, it is essential to consider the countermeasures for preventing/mitigating natural disasters in the city planning and construction stages.
In this article, the major natural disasters related to urban areas are discussed. The natural disasters considered are: flooding, heavy rain-induced slope failure (for cities adjacent to hills/mountains); earthquakes causing structure failure/collapse, and land subsidence (which is regarded as a natural disaster here despite being caused mainly by human involvement). For each type of disaster, first, its characteristics in recent years are described with some examples, and then the countermeasures are discussed.
To facilitate this review article, research publications related to the four natural disaster types from the Web of Science database from 2012 to 2021 were downloaded and analysed using VOSviewer [6]. The results of a brief bibliometric analysis are presented at the end of each topic.
2 Flooding
2.1 Occurrence tendency with some examples
Flooding is a major natural disaster occurring in urban areas. Sustainable flooding risk management is an important component of sustainable city development [7]. Urban flooding occurs when the stormwater increase rate exceeds the capacity of a city’s drainage system (e.g. [8]). One of the consequences of global warming is the increased frequency and intensity of torrential rain or storms. If a city’s drainage system does not consider this type of extreme rainfall, floods can easily occur. For example, in Beijing, the capital city of China, heavy flooding has occurred at least seven times over the past two decades. Among them, one case of flooding occurred on 21 July 2012, affected an area of 160,000 km2 and 1.9 million residents, and caused 77 casualties. In the city centre area, the average daily rainfall was 215 mm [9].
In 2017, the Japan Meteorological Agency (JMA) introduced a new term for the phenomenon of intensive rainfall in a narrow belt area: “a belt zone of heavy precipitation” [5], which is caused by cumulonimbus clouds occurring linearly one after another, passing through and staying in almost the same place; and as a result, very heavy rain continues to fall in a specific belt area for a long time. Beginning in 2022, the JMA started to forecast the occurrence of belt zones of heavy precipitation. In Japan, in recent years, belt zones of heavy precipitation-induced floods have occurred almost every year. On 3–4 July 2020, a belt zone of heavy precipitation occurred in the Kuma River basin region, Kumamoto, Japan, with an accumulated rainfall of 400–500 mm, which caused the collapse of the bank of the Kuma River and serious flooding in the area. This flood caused 65 casualties; 2 people were missing, 557 houses were completely destroyed, 43 were partially destroyed, 5,895 were flooded above the beds on the first floor, and 1,990 were flooded below the beds on the first floor [10].
2.2 Countermeasures
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Natural flood management (NFM)
NFM emphasises by protecting, restoring, and emulating the natural processes such as catchments, floodplains, rivers and the coastlines to reduce the potential for flooding or mitigate the effects of floods [11].
One of the main reasons for the flood that occurred in Yamagata Prefecture, Japan, on 30 August 2022 was poor maintenance resulting in many agricultural ponds (Nippon Hoso Kyokai (NHK) news, 1 September 2022).
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Engineering methods
Engineering methods mainly consist of the following.
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Building new drainage channels or increasing the capacity of existing drainage channels in a city [9].
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Reinforcing river banks to prevent water overflow from rivers.
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Constructing retarding basins to reduce the surface runoff velocity and, therefore, the rate of water flow into rivers or urban centres.
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Increasing the pump capacity in lowland areas, such as the Saga Plain, Japan, where in some areas. the elevation of the land is lower than the riverbed, and the rainfall water must be pumped into the river or sea.
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Soft countermeasures
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Development of early and accurate flood prediction techniques and warning systems. With the rapid development of computational capacity and high-resolution satellite imagery, early and accurate weather forecasting has gradually become possible. Based on the results of earlier predictions, earlier warning system can be established (e.g. [12, 13]).
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Create detailed high-accuracy flood hazard maps, educate residents to understand them, and effectively use them for evacuation.
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2.3 Brief bibliometric analysis results
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Identified Research cluster
The search strategy code was theme = (flooding) AND ((urban area). The four top research clusters were identified as follows:
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Cluster 2: urban flood management and mitigation (e.g. [19,20,21,22,23,24,25,26,27]).
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Cluster 3: simulation of flood evolution and inundation (e.g. [28,29,30,31,32,33,34,35,36]).
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Cluster 4: leading cause of urban flooding (e.g. [37,38,39,40,41,42,43,44,45]).
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Number of publications and top 10 journals publishing the papers.
Figure 1 shows the yearly number of research articles related to urban flooding published from 2012 to 2021. The number of articles has increased because of the increasing number of floods caused by extreme rainfall since the 2010s [46]. Table 1 shows the top ten journals in which research papers related to flooding were published.
3 Rainfall-induced slope failures/landslides
3.1 Occurrence and social impacts
Schuster [47] reported that the annual economic losses attributable to slope fluctuations in Japan and Italy are around US$ 4.5 billion per year, US$ 2.6 billion per year, respectively. Landslide activity is increasing because of changing climate patterns result in increased regional precipitation [47]. In recent years, the number of rainfall-induced slope collapses and landslides has increased owing to the increased frequencies of extreme rainfalls. The Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan [48] reported that the total number of sediment disasters that occurred in Japan in 2018 was 3,459, of which approximately 70% were rainfall-induced landslides and slope collapses. There were 161 casualties and 117 injuries.
Many cities are located in landslide-prone areas, such as Hong Kong, where landslides and slope failures occur almost every year [49]. The most famous landslides in Hong Kong were a string of large landslides that occurred in June 1972 [50]. The landslides occurred because of the severe rain that pounded Hong Kong in the days leading up to them. It is estimated that at least 156 people were killed, and several apartment buildings and houses were destroyed. Particularly, a "mammoth" downpour of around 640 mm in 72 h precipitated the tragedies on 18 June 1972, which resulted in the collapse of a luxury block in the Mid-Levels and the loss of 67 lives.
At around 10:30 am (JST) on 3 July 2021, a large-scale slope failure and debris flow occurred in the Aibatsu River in the mountain district of Izu, Atami City, Shizuoka Prefecture, Japan [51]. 27 people died (including one disaster-related death, and one missing person). According to the Japan Meteorological Agency, 2–3 days before the disaster, warm and moist air flowed continuously toward a stagnant front from western to eastern of Japan, and the atmospheric conditions became extremely unstable at the site. At the observation point in Ajiro, Atami City, which was relatively close to the site, 321 mm of precipitation was recorded in 48 h, ending at 3:20 pm on 3 July. Upstream of the river, there was land developed on a sloped area by embankments. Forensic investigation showed that the fill of the embankments was not properly compacted, was in a loose state, was almost saturated by the rainstorm, and thus failed first. Successive heavy rain then brought the failed embankments into a debris flow.
3.2 Predicting rainfall-induced landslides
Depending primarily on the hydromechanical qualities of the associated soils, rainfall-induced landslides can occur during brief, strong precipitation or after lengthy periods of rain. There are two types of rainfall-induced landslides: shallow and deep-seated position. Rainfall-induced deep-seated landslides (e.g. [52]) are generally caused by rainfall-induced groundwater level rise, which causes positive pore water pressures along the surface with the risk for slipping. This situation is normally associated with not only surface precipitation, but also the formation of preferential water flow pathways to bring surface rainfall into deeper locations of a slope, such as the formation of vertical cracks. There are two trigging mechanisms for shallow landslides [53]. One is rainfall precipitation into the surface of unsaturated soil layers, which reduces the suction,therefore, the decrease in shear strength of the soil layers causes landslides. Additionally, pore water pressures inside slopes are raised resulting from the formation of water tables, which occurs when a permeable soil layer rests above a substantially lower permeable layer. Contel et al. [53] proposed a straightforward criterion to identify the potential initiating mechanism of shallow landslides triggered by rainfall in accordance with the slope geometry, weight of unit, and strength characteristics of the concerned soil, as follows:
where α is slope angle; Z is the depth of potential failure; γ is the unit weight of soil; c' is the cohesion intercept under effective stress, and ϕ' denotes the effective stress angle of the soil's resistance to shearing. If SF d ≤ 1, a landslide may be induced at a depth Z due to a decrease in the initial suction caused by rainwater infiltration. If SF d > 1, landslides can only occur when the potential failure surface is subjected to positive pore water pressures due to rainfall.
To predict rainfall-induced landslides, rainfall precipitation (seepage) analysis and/or coupled seepage and stress–strain analysis of unsaturated soil slopes must be performed [54], in which numerical simulations, such as finite difference or finite element methods, are normally required. These techniques are described elsewhere and will not be explained here. To perform this type of analysis, hydromechanical properties, especially soil hydraulic characteristics under unsaturated conditions, that is, soil water characteristic curve (SWCC) and permeability functions, are required. Figures 2 and 3 show the typical SWCC and permeability functions of unsaturated soils [54].
The most commonly used SWCC functions are those proposed by Van Genuchten [55] and by Fredlund and Xing [56]. Although several permeability functions exist in the literature, a generally adopted method is to deduce them from SWCC functions,for example, the permeability function [57] can be combined with and SWCC function [55], which has an explicit expression,and Fredlund et al.’s [58] method, which does not have an explicit expression (an integration form). In these mathematical functions, fitting parameters must be determined using measured data. However, to measure the SWCCs of soils, special equipment is required, and this process is time-consuming (sometimes it may take several months). Therefore, in routing geotechnical site investigations, measuring the SWCCs of soils is not included. This type of situation limits or restricts the application of unsaturated soil mechanics in geotechnical designs. To provide a pragmatic solution, Chai and Gao [59] and Gao and Chai [60] proposed empirical methods to estimate the fitting parameters in the SWCCs of Fredlund and Xing [56] and Van Genuchten [55] using easily measured basic soil properties, such as saturated permeability, grain size distribution curve, and plastic index. It is believed that this type of estimation can effectively promote the application of unsaturated soil mechanics in predicting rainfall-induced slope failure or landslides.
Several factors influence rainfall-induced shallow slope failures. Except for the geometry and hydromechanical properties of the slope, the most important factors are the rainfall rate, rainfall duration, and initial moisture content of the slope soil preceding a rainstorm. Figure 4 illustrates a chart for predicting whether rainfall will induce a shallow slope failure. In the figure, the “lower” and “higher” initial moisture contents are qualitative or relative conditions. Defining precise values is considered impractical. For a given slope, the type of chart can be proposed by using the hydromechanical characteristics of the slope soil and the findings of precipitation analysis. In a region, the natural slopes may have different slope angles, different thicknesses of weathered soil layers, and different vegetation conditions. In practice, the slopes can be classified into several groups, and a prediction chart for each group can be proposed. Then, using weather forecast information on the rainfall rate and duration, possible rainfall-induced slope failures can be predicted.
Deep-seated rainfall-induced slope failures are more difficult to predict. They are mainly influenced by the hydrogeology of a slope, and sometimes a site investigation cannot reveal all the relevant details.
3.3 Methods for preventing/mitigating rainfall-induced slope failure
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Engineering/vegetation methods
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Failure of shallow slopes caused by rainfall
For rainfall-induced shallow slope failures, the most effective countermeasures are: (i) increasing the vegetation of a slope, and (ii) making the slope angle gentler. Other engineering methods include spraying a thin layer of concrete on the slope and rebar insertion methods [61].
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Failure of deep-seated slope
Countermeasures could be classified into two categories: methods of increasing the shear strength on a potential slip surface and/or reducing the driving force (moment) of a slope; and methods increasing the resistance force (moment) using other structures. The former methods include improving drainage systems for surface water and groundwater, removing certain soils from the upward part of a slope, and adding certain soils at the toe of a slope. The latter methods include the anchor, resistance pile, and retaining wall methods [62]. Normally, these methods are applied when there is a sign of slope instability.
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Soft methods
Soft methods aim to prevent or mitigate slope failure-induced damage to human lives and property. One method is to evacuate people from higher-risks areas, and if that is not feasible, another method is to establish a high-accuracy warning system.
3.4 Brief bibliometric analysis results
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Top four identified research clusters
The search strategy code used was: theme = (rainfall OR rain OR precipitation) AND (slope) AND (failure OR landslide OR lapse). The four top research clusters are as follows:
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Cluster 1: failure mechanisms of landslides (e.g. [63,64,65,66,67,68]).
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Cluster 2: landslide susceptibility evaluation (LSA) (e.g. [69,70,71]).
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Cluster 3: prediction of rainfall-induced shallow landslides (e.g. [72,73,74,75,76,77,78,79]).
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Cluster 4: soil erosion and landslide-type debris flow (e.g. [12, 80,81,82,83]).
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Number of publications and top 10 journals publishing the papers
Figure 5 presents the number of publications on rainfall-induced landslides from 2012 to 2021. From 2014 to 2021, the number of related papers rapidly increased, indicating increased research activity in this area. Table 2 lists the top 10 journals in which landslide-related papers were published from 2012 to 2021.
4 Earthquake
4.1 Occurrence and social impacts
It is commonly accepted that an earthquake is normally caused by “elastic rebound” of Earth’s crust, a theory proposed by Reid [84]. This theory states that a major cause of earthquakes is deformation due to external forces, primarily regional tectonic pressures. When the total amount of strain (energy) at a critical spot surpasses the capacity of the rock to withstand further strain, an earthquake will occur due to brittle fractures in competent rock or slip on preexisting weak zones. An earthquake fault is a region where the slip or displacement occurs [85].
Considerable attempts have already been made to predict the occurrence of an earthquake using observations of abnormal animal activities as well as monitored strain in bedrock (e.g. [86]. Although there have been some “successful” predictions, such as the Haicheng earthquake in 1975 (M 7.3) in China [87], generally, earthquakes still cannot be predicted precisely enough to avoid damage. The World health Organization [88] reported that nearly 750, 000 people worldwide died as a result of earthquakes between 1998–2017. In the emergency phase of the disaster, more than 125 million people were injured, made homeless or displaced affected by earthquakes during this period. If an earthquake occurs in a highly populated urban area, the damage to human lives and property is much larger than that in a rural area. To reduce or mitigate possible earthquake-induced damage, developing resilient structures for houses and public buildings has become a very important topic of research and city planning strategy for sustainable city development.
4.2 Resilient structures
MLIT [89], Japan, published the “Basis of Structural Design for Buildings and Public Works”, which stated that there are three basic performance requirements:
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Human life is protected from foreseeable events in and around structures.
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Structure functions are adequately protected from foreseeable threats.
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Restoring the structure within reasonable ranges of cost and time will enable it to continue to be used against foreseeable actions.
There are different detailed design considerations for the most important structures or buildings, such as nuclear power stations, and common office and/or department buildings. However, developing and innovating new techniques for structures resilient to earthquakes is a common and important task for sustainable city development.
Japan is an earthquake-prone country because it lies in the boundary region between the Eurasian and Pacific plates, and tectonic movement of the Plates causes earthquakes as well as active volcanoes. It is well known that many houses in Japan have wooden structures, which are more flexible and has better earthquake resistance compared to other common building types. However, they are more vulnerable to fire. It may be less well known that in Japan, most public buildings and apartment buildings have steel structures instead of reinforced concrete structures [90]. Steel structures for building have higher earthquake resistance but are more expansive.
Although there are ongoing studies about novel structures that are resilient against earthquakes, practically adopted resilient structures include earthquake-resistant structures, vibration-damping structures, and seismic isolation structures, as illustrated in Fig. 6. In earthquake-resistant structures, the pillars and beams that form the main frame of a building are strengthened to prevent the building from collapsing during an earthquake. On the other hand, for a seismic isolation structure, vibration isolation devices are installed under the structure, and for a vibration damping structure, dampers are installed between the beams and columns to absorb seismic energy and prevent damages to the main structures of a building [91].
4.3 Brief bibliometric analysis results
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Top four identified research clusters
The search strategy code was theme = (earthquake or temblor or seism or quake) AND ((urban area). The four top research clusters are as follows:
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Cluster 1: ground-motion parameters and prediction (e.g. [93,94,95]).
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Cluster 2: earthquake disaster response and evaluation (e.g. [96,97,98,99,100,101]).
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Cluster 3: earthquake-related geotectonic research (e.g. [98, 102, 103]).
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Cluster 4: the site amplification effect of seismic ground motion (e.g. [104,105,106,107]).
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Number of publications and top 10 journals publishing the papers
The yearly number of publications on earthquakes in urban areas from 2012 to 2021 is plotted in Fig. 7. From 2014 to 2021, papers related to earthquakes increased steadily, indicating with urbanisation, this subject has received increasing attention. Table 3 shows the top 10 journals in which the earthquake-related research papers were published from 2012 to 2021.
5 Land subsidence
5.1 Causes and social impact
The major reason of land subsidence or land-level lowering is excessive groundwater pumping [108]. It appears to occur naturally, but is a result of human activities. Land subsidence can damage infrastructure, increase flooding potential and damage drainage systems of cities, and it endangers human lives and property. With an increasing urban population (urbanisation), the global demand for freshwater increases every year. If there is insufficient fresh surface water, people will inevitably use groundwater, and if this usage is not well managed, it will induce land subsidence. Potential subsidence areas threaten 1.2 billion people and 21% of the major cities worldwide, especially in coastal areas. Among them, 86% of the exposed population lives in Asia. Figure 8 shows a map of potential areas of land subsidence in East Asia. United Nations Office for Disaster Risk Reduction (UNDRR) asserts this study enhances subsidence comprehension, uncovers new subsidence areas, and informs mitigation strategies [109]. This clearly shows that Eastern China has the highest potential in this region. Therefore, for sustainable city development, an economical and effective plan to ensure safe access to water resources is essential.
Shanghai is the largest economic city in China and has suffered serious land subsidence problems. As seen in Fig. 9, The deltaic deposit of the Yangtze River forms the foundation of Shanghai, as illustrated in Fig. 9. The primary geological layers are shown in Fig. 10, and Quaternary deposits are approximately 300 m thick. In Shanghai, land subsidence was brought on by an excessive amount of ground water pumping, which also caused the Quaternary deposit to become compressed. In 1921, Shanghai established a programme to monitor the subsidence of the soil. Up until the year 2000, the centre part of Shanghai had a cumulative subsidence of between 2 and 3 m (Fig. 11). In Fig. 11, the subsidence of land in the urban area of Shanghai can be divided into two distinct time periods, the rapid subsidence era, which occurred between the years 1921 and 1965, and the regulated phase, which occurred after 1965. From 1965, pumping of groundwater in the Shanghai area was strictly controlled by the local government, and the land subsidence was clearly mitigated. Land subsidence in Shanghai has caused many social issues. An increase in the likelihood of flooding is the issue that needs to be addressed right away. Flooding due to precipitation occurred 22 times between 1981 and 1994, which is an average of nearly twice per year [111]. Recently, there has been a concurrent rise in the likelihood of floods caused by tides. Dike heights along the coastline increased four times from 1956 to 1960, with crest elevations rising from 5 to 6.8 m. Damage to sewerage systems, roads, buildings, and underground tunnels are among the other issues created by land subsidence.
5.2 Countermeasures to maintain sustainability of cities
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Regional and/or national water management
In most regions or countries, the distribution of freshwater resources is unbalanced;freshwater may be rich in certain parts and lacking in other parts. It is essential for local governments to efficiently and economically manage water resources.
In China, freshwater is rich (more than enough) in the South, but in the North, there is not enough freshwater. To solve this type of national freshwater shortage problem, the national project of the South-to-North Water Division (Fig. 12) was commenced in 2002; parts of it have been completed and parts are still under construction. It is one of the greatest projects in Chinese history, and it can mitigate freshwater shortages and therefore land subsidence problems in North-Eastern parts of China.
Quaternary clayey soils of 10 to 30 m thickness were deposited in the Saga Plain, Japan [115]. In Shiroishi District, Saga, the Quaternary soil layers were compressed due to excessive groundwater pumping for agricultural purposes and approximately 1.0 m of subsidence from 1970 to 2000. After that, there were restrictions on the amount of groundwater to be pumped, and land subsidence was mitigated. Furthermore, from 2012, surface water from Kasegawa Dam was diverted to Shiroishi District and pumping of groundwater was largely stopped, which prevented further land subsidence.
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Efficient use of water
With the increasing of world populations and limited freshwater resource, aside from diverting water from rivers into cities, recycling locally available water resources is an effective way to ensure water safety in urban areas and maintain sustainable development. A community can recycle its wastewater, such as from bathtubs and washing machines, to flush toilets, wash cars, and water plants. This could help reduce the amount of groundwater used in certain areas and mitigate possible land subsidence.
5.3 Brief bibliometric analysis results
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Top three identified research clusters
The search strategy code was theme = (land OR ground OR soil OR earth surface) AND (subsidence OR settlement) AND (urban area). The top three identified top three research clusters are as follows:
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Cluster 1: the possible causes of urban ground subsidence (e.g. [116,117,118]).
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Cluster 2: land subsidence monitoring techniques (e.g. [119,120,121]).
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Cluster 3: the land subsidence prediction model (e.g. [113, 117, 122, 123]).
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Number of publications and top 10 journals publishing the papers
Figure 13 presents the number of publications on land subsidence in urban areas from 2012 to 2021. Table 4 shows the top 10 journals in which the land subsidence-related papers were published from 2012 to 2021.
6 Perspectives for risk warning and hazards prevention
6.1 Strengthening field monitoring
Natural disaster monitoring is a prominent component of response strategies for natural hazards. Field monitoring not only enables the evaluation of the risks associated with these hazards, but also allows the optimization of their prevention. For instance, remote sensing technologies are highly effective for enhancing the understanding of spatial and temporal trends of phenomena such as flooding [44, 100], landslides [124, 125], earthquakes (..), and land subsidence [43, 126]. However, these technologies should be operated in parallel with traditional methods to provide explicit insights into the investigated systems. Tools such as multiple-sensor, high-precision GPS, radars and geodetic methods (e.g., levelling) generally help in natural object monitoring, while automatic monitoring systems allow the approximation of future seismological, climatic, and ground-water level changes associated with hazard occurrences. Furthermore, with technological breakthroughs, satellite technologies are increasing utilised for monitoring natural disasters. The primary advantage of this approach is its relatively large and movable coverage range. Adopted by agencies such as the Asia–Pacific Regional Space Agency Forum (APRSAF), the hybridisation of space-based technology and WEB-GIS technology has proven to be an efficient tool for disasters management [127]. Although strengthening field monitoring can improve the understanding of natural disasters, the assessment of the risk associated with these hazards as well as early warnings of their occurrence is imperative to achieve disaster resilience.
6.2 MCDM risk assessment
Disaster risk assessment in urban areas is crucial for long-term planning and policymaking processes for local communities. The aim of quantifying disaster risks is typically to determine their nature and magnitudes such as the probability of high-intensity flood [42], areas affected by landslides [128], seismic intensity [129], and degree of land subsidence [101]. This is carried out by analysing hazards and assessing the punctual states of vulnerability that could endanger exposed people and their immediate environment. In this regard, tremendous developments have been achieved in recent years to quantify the effects of disasters occurring in urban areas. Current state-of-the-art risk assessment approaches include probabilistic and statistical [130], fuzzy set ([131], [132], GIS-enabled zoning [133], and risk characterisation [134] methods. Nevertheless, a resurgence of some traditional frameworks (that are continuously improving) such as scenario analysis, comentropy, or grey system techniques has also been observed in recent years [135]. Because the precision of disaster risk assessment is primarily contingent on the data quality and spatial–temporal coverage of the assessment model, existing methods inherently embody or generate constraints that limit their efficiency in assessing the true exposure of people and their environment to natural disaster risks. Focusing on climate risk assessment, Arribas et al. [136] argued that improvements should be made to consider compounding risks, comprehensive databases, and comparison/combinations of different assessments results.
6.3 AI-enabled early warning systems
The stochastic nature of disaster phenomena has driven global efforts to target the integration of AI in disaster-management systems. AI possesses huge potential for strengthening disaster mitigation owing to the seamless availability of data and the increasing performance of forecasting algorithms. These two features are critical for enabling early warning systems, that is, technologies and processes that aim to predict and mitigate the harm of natural disasters. The data must be sufficient, representative, precise (e.g. in terms of resolution), and consistent (e.g. sequential data, real-time data) with the operating algorithm. However, the rarity of some events, such as earthquakes, can hamper the creation of sufficiently large database for training algorithms. Kuglitsch et al. [137] postulated that producing synthetic data could be a viable solution to this problem. Furthermore, predicting algorithms have mainly adopted the deep learning paradigm because of its ability to explore massive design spaces, deal with nonlinearity, and identify multidimensional correlations. These algorithm can predict the future occurrence of hazards, which is critical for organising appropriate responses. Recent successes include deep learning-based ground characterisation [138,139,140,141,142], real-time and dynamic modelling [143,144,145,146]), ensemble models, and hybrid model-based forecasting of the occurrence of future phenomena [147, 148]. To take full advantage of these technologies, they must be integrated into an interdisciplinary platform to allow the seamless understanding of both the operating process and predictive analysis by relevant actors, including researchers, engineers, multi-stakeholders, and decision-makers.
7 Concluding remarks
In 2015, the UN set 17 goals for sustainable economic and social development. Goal 11 seeks to make cities accessible and safe for all people; Goal 13 seeks to immediately mitigate the effects of climate change. Preventing and mitigating natural disasters in urban areas can be an indirect part of these two goals. Four types of natural disasters—flooding, heavy rain-induced slope failures/landslides, earthquakes causing structure failure/collapse; and land subsidence—are considered in this study. The characteristics of each disaster in recent years and the possible countermeasures are also discussed.
With the increasing frequency of climate change-induced extreme weathers, the number of floods has increased in recent years in metropolitan regions. The countermeasures include natural flooding management, such as increasing vegetation coverage of lands, engineering methods for enhancing drainage capacities in urban areas, increasing elevation of dikes, and soft methods such as early warning systems and evacuation plans.
Heavy rain-induced slope failures/landslides affect cities adjacent to hills/mountains (such as Hong Kong). Again, extreme weather-induced torrential rains cause more slope failures and landslides. Engineering measures for preventing slope failures or landslides are often implemented after observing the signs of slope instability. Therefore, it is important to predict rainfall-induced slope failures/landslides. With an accurate early prediction/warning system, damage resulting from slope failures/landslides can be substantially mitigated.
Although considerable efforts have been made to predict earthquake, they still cannot be predicted effectively enough to avoid damage. Therefore, developing structures resilient to earthquakes is an essential engineering task for sustainable city development. Three types of structures can be adopted: earthquake-resistant structures, vibration damping structures, and seismic isolation structures.
Land subsidence is a human activity, that is, the main cause is excessive groundwater pumping. With rapid urbanisation, the demand for more freshwater is increasing worldwide, and some of the freshwater is obtained by pumping groundwater, which can cause land subsidence. Approximately, 1.2 billion people are affected by land subsidence worldwide. The corresponding countermeasures include effective regional and national freshwater management as well as locally water recycling.
To mitigate potential disasters in urban areas, the following perspectives are recommended: 1) establishment and strengthening of field monitoring systems, 2) conducting risk assessments and providing warnings based on multi-criteria decision making models, and 3) establishment of early warning systems based on artificial intelligence technologies.
Availability of data and materials
Data is available upon reasonable request.
References
Cohen B (2006) Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technol Soc 28:63–80. https://doi.org/10.1016/j.techsoc.2005.10.005
Degree of urbanization in China from 1980 to 2022. Statista Website. https://www.statista.com/statistics/270162/urbanization-in-china/. Accessed 17 April 2023
Kawata Y (1995) The great Hanshin-Awaji earthquake disaster: damage, social response, and recovery. J Nat Dis Sci 17(2):1–12
Ornes S (2018) How does climate change influence extreme weather? Impact attribution research seeks answers. PNAS 115(33):8232–8235. https://doi.org/10.1073/pnas.1811393115
Japan Meteorological Agency (JMA) (2017) Xnxx.com: a belt zone of heavy precipitation - Terminology used by Japan Meteorological Agency for rain related weather forecast. (in Japanese). https://www.jma.go.jp
van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3
Binns AD (2022) Sustainable development and flood risk management. J Flood Risk Manag 15(2):e12807. https://doi.org/10.1111/jfr3.12807
Anni AH, Cohen S, Praskievicz S (2020) Sensitivity of urban flood simulations to stormwater infrastructure and soil infiltration. J Hydrol 588:125028. https://doi.org/10.1016/j.jhydrol.2020.125028
Ma Y, Cui YT, Tan HG, Wang HY (2022) Case study: Diagnosing China’s prevailing urban flooding—Causes, challenges, and solutions. J Flood Risk Manag 15(3):e12822. https://doi.org/10.1111/jfr3.12822
Ohmoto T (2021) Report for comprehensive survey and research on Kyushu heavy rain disaster occurred in July 2020. Kumamoto University, Japan (in Japanese)
Samuels P (2022) The rise of natural flood management. J Flood Risk Manag 15(3):e12837. https://doi.org/10.1111/jfr3.12837
Guo J, Wang J, Li Y, Yi S (2021) Discussions on the transformation conditions of Wangcang landslide-induced debris flow. Landslides 18:1833–1843. https://doi.org/10.1007/s10346-021-01650-4
Guo ZF, Leitão JP, Simões NE, Moosavi V (2021) Data-driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks. J Flood Risk Manag 14(1):12684. https://doi.org/10.1111/jfr3.12684
Darabi H, Choubin B, Rahmati O, Haghighi AT, Pradhan B, Kløve B (2019) Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. J Hydrol 569:142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002
Luo P, Mu D, Xue H, Ngo-Duc T, Dang-Dinh K, Takara K, Nover D, Schladow G (2018) Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Sci Rep 8:12623. https://doi.org/10.1038/s41598-018-30024-5
Nedkov S, Burkhard B (2012) Flood regulating ecosystem services—Mapping supply and demand, in the Etropole municipality, Bulgaria. Ecol Ind 21:67–79. https://doi.org/10.1016/j.ecolind.2011.06.022
Ogie RI, Holderness T, Dunn S, Turpin E (2018) Assessing the vulnerability of hydrological infrastructure to flood damage in coastal cities of developing nations. Comput Environ Urban Syst 68:97–109. https://doi.org/10.1016/j.compenvurbsys.2017.11.004
Pachauri RK, Allen MR, Barros VR et al (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC, Geneva
Chan FKS, Chuah CJ, Ziegler A, Dąbrowski M, Varis O (2018) Towards resilient flood risk management for Asian coastal cities: Lessons learned from Hong Kong and Singapore. J Clean Prod 187:576–589. https://doi.org/10.1016/j.jclepro.2018.03.217
Chang LC, Chang FJ, Yang SN, Tsai FH, Chang TH, Herricks EE (2020) Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance. Nat Commun 11:1983. https://doi.org/10.1038/s41467-020-15734-7
Green D, O’Donnell E, Johnson M, Slater L, Thorne C, Zheng S, Stirling R, Chan FK, Li L, Boothroyd RJ (2021) Green infrastructure: The future of urban flood risk management? Wiley Interdiscip Rev Water 8(6):e1560. https://doi.org/10.1002/wat2.1560
Han S, Kuhlicke C (2021) Barriers and drivers for mainstreaming nature-based solutions for flood risks: The case of South Korea. Int J Disaster Risk Sci 12:661–672. https://doi.org/10.1007/s13753-021-00372-4
Lyu HM, Sun WJ, Shen SL, Arulrajah A (2018) Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Sci Total Environ 626(2018):1012–1025. https://doi.org/10.1016/j.scitotenv.2018.01.138
Ren S, Zhang B, Wang W-J, Yuan Y, Guo C (2021) Sedimentation and its response to management strategies of the Three Gorges Reservoir, Yangtze River China. Catena 199:105096. https://doi.org/10.1016/j.catena.2020.105096
Verol AP, Battemarco BP, Merlo ML, Machado ACM, Haddad AN, Miguez MG (2019) The urban river restoration index (URRIX)-A supportive tool to assess fluvial environment improvement in urban flood control projects. J Clean Prod 239:118058
Yousefi S, Pourghasemi HR, Emami SN et al (2020) Assessing the susceptibility of schools to flood events in Iran. Sci Rep 10:18114. https://doi.org/10.1038/s41598-020-75291-3
Zheng Q, Shen SL, Zhou A, Lyu HM (2022) Inundation risk assessment based on G-DEMATEL-AHP and its application to Zhengzhou flooding disaster. Sustain Cities Soc 86:104138. https://doi.org/10.1016/j.scs.2022.104138
Carlotto T, Chaffe PLB, dos Santos CI, Lee S (2021) SW2D-GPU: A two-dimensional shallow water model accelerated by GPGPU. Environ Model Softw. 145:105205. https://doi.org/10.1016/j.envsoft.2021.105205
Costabile P, Costanzo C, De Lorenzo G, Macchione F (2020) Is local flood hazard assessment in urban areas significantly influenced by the physical complexity of the hydrodynamic inundation model? J Hydrol 580:124231. https://doi.org/10.1016/j.jhydrol.2019.124231
Fraehr N, Wang QJ, Wu W, Nathan R (2022) Upskilling low-fidelity hydrodynamic models of flood inundation through spatial analysis and gaussian process learning. Water Resour Res 58:e2022WR032248. https://doi.org/10.1029/2022WR032248
Hosseini FS, Choubin B, Mosavi A, Nabipour N, Shamshirband S, Darabi H, Haghighi AT (2020) Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method. Sci Total Environ 711:135161. https://doi.org/10.1016/j.scitotenv.2019.135161
Luo P, Luo M, Li F, Qi X, Huo A, Wang Z, He B, Takara K, Nover D (2022) Urban flood numerical simulation: Research, methods and future perspectives. Environ Model Software 156:105478. https://doi.org/10.1016/j.envsoft.2022.105478
Munawar HS, Hammad AW, Waller ST (2021) A review on flood management technologies related to image processing and machine learning. Autom Construction 132:103916. https://doi.org/10.1016/j.autcon.2021.103916
Nevo S, Morin E, Gerzi Rosenthal A et al (2022) Flood forecasting with machine learning models in an operational framework. Hydrol Earth Syst Sci 26:4013–4032. https://doi.org/10.5194/hess-26-4013-2022
Wu Y, Peng F, Peng Y, Kong X, Liang H, Li Q (2019) Dynamic 3D simulation of flood risk based on the integration of spatio-temporal GIS and hydrodynamic models. ISPRS Int J Geo Inf 8:520. https://doi.org/10.3390/ijgi8110520
Yang Y, Sun L, Li R, Yin J, Yu D (2020) Linking a storm water management model to a novel two-dimensional model for urban pluvial flood modeling. Int J Disaster Risk Sci 11:508–518. https://doi.org/10.1007/s13753-020-00278-7
Avashia V, Garg A (2020) Implications of land use transitions and climate change on local flooding in urban areas: An assessment of 42 Indian cities. Land Use Policy 95:104571. https://doi.org/10.1016/j.landusepol.2020.104571
Balogun AL, Marks D, Sharma R, Shekhar H, Balmes C, Maheng D, Arshad A, Salehi P (2020) Assessing the potentials of digitalization as a tool for climate change adaptation and sustainable development in urban centres. Sustain Cities Soc 53:101888. https://doi.org/10.1016/j.scs.2019.101888
de Macedo LSV, Picavet MEB, de Oliveira JAP, Shih W-Y (2021) Urban green and blue infrastructure: A critical analysis of research on developing countries. J Clean Product 313:127898. https://doi.org/10.1016/j.jclepro.2021.127898
Huang X, Wang D, Ziegler AD, Liu X, Zeng H, Xu Z, Zeng Z (2022) Influence of urbanization on hourly extreme precipitation over China. Environ Res Lett 17:044010. https://doi.org/10.1088/1748-9326/ac59a6
Lv H, Guan X, Meng Y (2020) Comprehensive evaluation of urban flood-bearing risks based on combined compound fuzzy matter-element and entropy weight model. Nat Hazards 103:1823–1841. https://doi.org/10.1007/s11069-020-04056-y
Lyu HM, Shen SL, Yang J, Yin ZY (2019) Inundation analysis of metro systems with the storm water management model incorporated into a geographical information system: a case study in Shanghai. Hydrol Earth Syst Sci 23:4293–4307. https://doi.org/10.5194/hess-23-4293-2019
Lyu HM, Shen SL, Zhou AN, Yang J (2019) Perspectives for flood risk assessment and management for mega-city metro system. Tunnel Underground Space Technol 84(2019):31–44. https://doi.org/10.1016/j.tust.2018.10.019
Lyu HM, Shen SL, Zhou AN, Zhou WH (2019) Flood risk assessment of metro systems in a subsiding environment using the interval FAHP-FCA approach. Sustain Cities Soc 50:101682. https://doi.org/10.1016/j.scs.2019.101682
Mei C, Liu J, Wang H, Li Z, Yang Z, Shao W, Ding X, Weng B, Yu Y, Yan D (2020) Urban flood inundation and damage assessment based on numerical simulations of design rainstorms with different characteristics. Sci China Technol Sci 63:2292–2304. https://doi.org/10.1007/s11431-019-1523-2
Wang W, Yang S, Stanley HE, Gao J (2019) Local floods induce large-scale abrupt failures of road networks. Nat Commun 10:2114. https://doi.org/10.1038/s41467-019-10063-w
Schuster RL (1996) Socioeconomic significance of landslides. Landslides: Investigation and Mitigation, Special Report 247. Transportation Research Board, Washington, pp 12–35
Junichi K, Naoki I (2020) Outline of measures for sediment disaster by the Sabo department of MLIT. Japan Landslides 17(11):2503–2513. https://doi.org/10.1007/s10346-020-01554-9
Hencher S, Malone A (2012) Hong Kong landslides. In: Clague J., Stead D. (eds) Landslides: Types, Mechanisms and Modeling. Cambridge University Press, Cambridge, pp 373–382. https://doi.org/10.1017/CBO9780511740367.031
Yang TL, Mackey S, Cumine E (2008) Final report of the commission of inquiry into the rainstorm disasters 1972. Geo Report No. 229. Geotechnical Engineering Office, Civil Engineering Department, Hong Kong
Yanagawa Y, Nagasawa H, Morishima K et al (2021) Activity of a Medical Relief Team from Shizuoka Hospital that was Dispatched to the Atami Debris Flow in 2021. Juntendo Med J 67(6):542-546
Gerscovich DMS, Vargas EA, de Campos TMP (2006) On the evaluation of unsaturated flow in a natural slope in Rio de Janeiro Brazil. Eng Geol 88(1–2):23–40. https://doi.org/10.1016/j.enggeo.2006.07.008
Contel E, Pugliese E, Troncone A (2022) A simple method for predicting rainfall-induced shallow landslides. J Geotech Geoenviron Eng 148(10):04022079. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002877
Fredlund DG (2006) Unsaturated soil mechanics in engineering practice. J. Geotech Geoenviron Eng 132(3):286–321. https://doi.org/10.1061/(ASCE)1090-0241(2006)132:3(286)
Van Genuchten MT (1980) A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Sci Soc Am J 44:892–898. https://doi.org/10.2136/sssaj1980.03615995004400050002x
Fredlund DG, Xing AQ (1994) Equations for the soil-water characteristic curve. Can Geotech J 31:521–532. https://doi.org/10.1139/t94-061
Mualem Y (1978) Hydraulic conductivity of unsaturated porous media: Generalized macroscopic approach. Water Resour Res 14:325–334. https://doi.org/10.1029/WR014i002p00325
Fredlund DG, Xing AQ, Huang XY (1994) Predicting the permeability function for unsaturated soils using the soil-water characteristic curve. Can Geotech J 31:533546. https://doi.org/10.1139/t94-062
Chai JC, Gao ZY (2021) Method for predicting drying-wetting and scanning soil-water characteristic curves. Transport Geotech 31:100666. https://doi.org/10.1016/j.trgeo.2021.100666
Gao ZY, Chai JC (2022) Method for predicting unsaturated permeability using basic soil properties. Transport Geotechnics 34:100754. https://doi.org/10.1016/j.trgeo.2022.100754
Joint Venture of Nippon Koei Co. Ltd. and OYO International Corporation (2007) The study on risk management for sediment-related disaster on selected national highways in the republic of the Philippines: Final report, Guide III – Guide to road slope protection. A Project of Japan International Cooperation Agency, Tokyo, Japan. https://openjicareport.jica.go.jp/614/614/614_118_11856424.html
Choi KY, Cheung R (2013) Landslide disaster prevention and mitigation through works in Hong Kong. J Rock Mech Geotech Eng 5(5):354–365. https://doi.org/10.1016/j.jrmge.2013.07.007
Guo C, Zhang Y, Yuan H, Liu D, Yan Y, Hua S, Ren S (2022) Study of an ancient landslide reactivation mechanism based on centrifuge model testing: an example of the Jiangdingya ancient landslide reactivation in 2018, Gansu Province, China. Landslides 20:127–141. https://doi.org/10.1007/s10346-022-01978-5
Sun Sw, Liu L, Hu Jb, Ding H (2022) Failure characteristics and mechanism of a rain-triggered landslide in the northern longwall of Fushun west open pit, China. Landslides 19:2439–2458. https://doi.org/10.1007/s10346-022-01926-3
Sattler K, Elwood D, Hendry M et al (2021) Quantifying the contribution of matric suction on changes in stability and displacement rate of a translational landslide in glaciolacustrine clay. Landslides 18:1675–1689. https://doi.org/10.1007/s10346-020-01611-3
Tan X, Huang Y, Xiong D, Lv K, Chen F (2020) The effect of Elymus nutans sowing density on soil reinforcement and slope stabilization properties of vegetation-concrete structures. Sci Rep 10:20462. https://doi.org/10.1038/s41598-020-77407-1
Yang Z, Cai H, Shao W, Huang D, Uchimura T, Lei X, Tian H, Qiao J (2019) Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis. Bull Eng Geol Env 78:2139–2150. https://doi.org/10.1007/s10064-018-1295-5
Zhu Y, Ishikawa T, Zhang Y, Nguyen BT, Subramanian SS (2022) A FEM-MPM hybrid coupled framework based on local shear strength method for simulating rainfall/runoff-induced landslide runout. Landslides 19(8):2021–2032. https://doi.org/10.1007/s10346-022-01849-z
Gong B (2021) Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization. Sci Rep 11:17888. https://doi.org/10.1038/s41598-021-97484-0
Lyu HM, Shen JS, Arulrajah A (2018) Assessment of Geohazards and Preventative Countermeasures Using AHP Incorporated with GIS in Lanzhou China. Sustainability 10:304. https://doi.org/10.3390/su10020304
Zheng Q, Lyu HM, Zhou A, Shen SL (2021) Risk assessment of geohazards along Cheng-Kun railway using fuzzy AHP incorporated into GIS. Geomat Nat Haz Risk 12:1508–1531. https://doi.org/10.1080/19475705.2021.1933614
Cao Y, Wei X, Fan W, Nan Y, Xiong W, Zhang S (2021) Landslide susceptibility assessment using the weight of evidence method: A case study in Xunyang area China. PloS one 16:e0245668. https://doi.org/10.1371/journal.pone.0245668
Fang K, Tang H, Li C, Su X, An P, Sun S (2022) Centrifuge modelling of landslides and landslide hazard mitigation: A review. Geosci Front 14(1):101493. https://doi.org/10.1016/j.gsf.2022.101493
Huang C, Sun Y, An Y, Shi C, Feng C, Liu Q, Yang X, Wang X (2022) Three-dimensional simulations of large-scale long run-out landslides with a GPU-accelerated elasto-plastic SPH model. Eng Anal Boundary Elem 145:132–148. https://doi.org/10.1016/j.enganabound.2022.09.018
Jia G, Tang Q, Xu X (2020) Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings. Landslides 17:283–299. https://doi.org/10.1007/s10346-019-01277-6
Li Y, Cui P, Ye C, Junior JM, Zhang Z, Guo J, Li J (2021) Accurate prediction of earthquake-induced landslides based on deep learning considering landslide source area. Remote Sensing 13(17):3436. https://doi.org/10.3390/rs13173436
Marin RJ, Velásquez MF (2020) Influence of hydraulic properties on physically modelling slope stability and the definition of rainfall thresholds for shallow landslides. Geomorphology 351:106976. https://doi.org/10.1016/j.geomorph.2019.106976
Medina V, Hürlimann M, Guo Z, Lloret A, Vaunat J (2021) Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale. Catena 201:105213. https://doi.org/10.1016/j.catena.2021.105213
Uchimura T, Towhata I, Wang L, Nishie S, Yamaguchi H, Seko I, Qiao J (2015) Precaution and early warning of surface failure of slopes using tilt sensors. Soils Found 55(5):1086–1099. https://doi.org/10.1016/j.sandf.2015.09.010
Chen H, Zhang X, Abla M, Lü D, Yan R, Ren Q, Ren Z, Yang Y, Zhao W, Lin P (2018) Effects of vegetation and rainfall types on surface runoff and soil erosion on steep slopes on the Loess Plateau, China. CATENA 170:141–149. https://doi.org/10.1016/j.catena.2018.06.006
Dahlquist MP, West AJ (2019) Initiation and runout of post-seismic debris flows: insights from the 2015 Gorkha earthquake. Geophys Res Lett 46:9658–9668. https://doi.org/10.1029/2019GL083548
Li R, Zhang W, Yang S, Zhu M, Kan S, Chen J, Ai X, Ai Y (2018) Topographic aspect affects the vegetation restoration and artificial soil quality of rock-cut slopes restored by external-soil spray seeding. Sci Rep 8:12109. https://doi.org/10.1038/s41598-018-30651-y
Svennevig K, Hermanns RL, Keiding M et al (2022) A large frozen debris avalanche entraining warming permafrost ground—the June 2021 Assapaat landslide, West Greenland. Landslides 19:2549–2567. https://doi.org/10.1007/s10346-022-01926-3
Lawson AC, Reid HF (1908) The California earthquake of April 18, 1906: report of the state earthquake investigation commission (No. 87). Carnegie institution of Washington, Washington, D.C.
Snoke JA (1990) Earthquake mechanisms. In: Geophysics. Encyclopedia of Earth Science. Springer, Boston, pp 12–35. https://doi.org/10.1007/0-387-30752-4_2
Mogi K (1985) Earthquake prediction. Academic press, Tokyo
Davies D (1975) Earthquake prediction in China. Nature 258:286–287
Earthquakes. WHO website (2022) https://www.who.int/health-topics/earthquakes. Accessed 17 Apr 2023
Ministry of Land, Infrastructure and Transport (2002) Basis of Structural Design for Buildings and Public Works. Japan Society of Civil Engineers Website. https://www.jsce-int.org/system/files/Basis_of_Structural_Design(MLIT).pdf. Accessed 17 April 2023
Kanno R (2017) Advances in steel structures and steel materials in Japan. ce/papers 1:394–404. https://doi.org/10.1002/cepa.539
NHK (2022) Differences between seismic isolation, vibration damping, and earthquake resistance. https://www.menshin.biz/?q=menshin/node/4840#. Accessed 17 April 2023
NSC (2022) What is seismic isolation device? https://www.eng.nipponsteel.com/steelstructures/product/base_isolation/. Accessed 17 April 2023
Goto H, Kaneko Y, Young J, Avery H, Damiano L (2019) Extreme accelerations during earthquakes caused by elastic flapping effect. Sci Rep 9:1117. https://doi.org/10.1038/s41598-018-37716-y
Minson SE, Baltay AS, Cochran ES, Hanks TC, Page MT, McBride SK, Milner KR, Meier MA (2019) The limits of earthquake early warning accuracy and best alerting strategy. Sci Rep 9(1):2478. https://doi.org/10.1038/s41598-019-39384-y
Picozzi M, Bindi D, Spallarossa D, Di Giacomo D, Zollo A (2018) A rapid response magnitude scale for timely assessment of the high frequency seismic radiation. Sci Rep 8:8562. https://doi.org/10.1038/s41598-018-26938-9
Gonzales H, López-Almansa F (2012) Seismic performance of buildings with thin RC bearing walls. Eng Struct 34:244–258. https://doi.org/10.1016/j.engstruct.2011.10.007
Jiang N, Cheng J, Ni Z, Ye Y, Hu R, Jiang X (2021) Developing a new individual earthquake resilience questionnaire: A reliability and validity test. PloS one 16:e0245662. https://doi.org/10.1371/journal.pone.0245662
Kitayama S, Constantinou MC (2018) Seismic performance of buildings with viscous damping systems designed by the procedures of ASCE/SEI 7–16. J Struct Eng 144:04018050. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002048
Lyu HM, Shen SL, Yang J, Zhou AN (2020) Risk assessment of earthquake-triggered geohazards surrounding Wenchuan China. Nat Hazards Rev 21:05020007. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000375
Lyu HM, Shen SL, Zhou AN, Yang J (2020) Risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP. Sci Total Environ 717:135310. https://doi.org/10.1016/j.scitotenv.2019.135310
Lyu HM, Zhou WH, Shen SL, Zhou AN (2020) Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen. Sustain Cities Soc 56:102103. https://doi.org/10.1016/j.scs.2020.102103
Agata R, Barbot SD, Fujita K, Hyodo M, Iinuma T, Nakata R, Ichimura T, Hori T (2019) Rapid mantle flow with power-law creep explains deformation after the 2011 Tohoku mega-quake. Nat Commun 10:1385. https://doi.org/10.1038/s41467-019-08984-7
Chen CH, Wen S, Yeh TK, Wang CH, Yen HY, Liu JY, Hobara Y, Han P (2013) Observation of surface displacements from GPS analyses before and after the Jiashian earthquake (M=6.4) in Taiwan. J Asian Earth Sci 62:662–671. https://doi.org/10.1016/j.jseaes.2012.11.016
Abbas M, Elbaz K, Shen SL, Chen J (2021) Earthquake effects on civil engineering structures and perspective mitigation solutions: a review. Arab J Geosci 14:1350. https://doi.org/10.1007/s12517-021-07664-5
AtanganaNjock PG, Zhou A, Lyu HM (2020) Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t-SNE model. Soil Dynamics Earthquake Eng 130:105988. https://doi.org/10.1016/j.soildyn.2019.105988
Zhu C, Pilz M, Cotton F (2020) Evaluation of a novel application of earthquake HVSR in site-specific amplification estimation. Soil Dynamics Earthquake Eng 139:106301. https://doi.org/10.1016/j.soildyn.2020.106301
Zhu C, Pilz M, Cotton F (2020) Which is a better proxy, site period or depth to bedrock, in modelling linear site response in addition to the average shear-wave velocity? Bull Earthq Eng 18:797–820. https://doi.org/10.1007/s10518-019-00738-6
Chai JC, Shen SL, Zhu HH, Zhang XL (2004) Land subsidence due to groundwater drawdown in Shanghai. Geotechnique 54(2):143–147. https://doi.org/10.1680/geot.2004.54.2.143
United Nations Office for Disaster Risk Reduction (UNDRR) (2021) Global map shows nineteen percent of the global population may face a high probability of subsidence. https://www.preventionweb.net/news/global-map-shows-nineteen-percent-global-population-may-face-high-probability-subsidence.
Herrera-García G, Ezquerro P, Tomás R et al (2021) Mapping the global threat of land subsidence. Science 371:34–36. https://doi.org/10.1126/science.abb8549
Liu Y (2001) Countermeasures and their effect for land subsidence in Shanghai. Proc. of 6th Inter. Symposium on Land subsidence. Chinese Geology Press, Ravenna, pp 75–79 (in Chinese)
Xu YS, Shen SL, Du YJ (2009) Geological and hydrogeological environment in Shanghai with geohazards to construction and maintenance of infrastructures. Eng Geol 109(3–4):241–254. https://doi.org/10.1016/j.enggeo.2009.08.009
Shen SL, Ma L, Xu YS, Yin ZY (2013) Interpretation of increased deformation rate in aquifer IV due to groundwater pumping in Shanghai. Can Geotech J 50(11):1129–1142. https://doi.org/10.1139/cgj-2013-0042
Ministry of Water Resources of the People’s Republic of China. Overview of the South-to-North Water Diversion Project. http://nsbd.mwr.gov.cn/zw/gcgk/.
Chai JC, Hino T, Shen SL (2017) Characteristics of clay deposits in Saga Plain, Japan. Geotechn Eng Proceed Institution Civil Eng 170(6):548–558. https://doi.org/10.1680/jgeen.16.00197
El Shinawi A, Kuriqi A, Zelenakova M, Vranayova Z, Abd-Elaty I (2022) Land subsidence and environmental threats in coastal aquifers under sea level rise and over-pumping stress. J Hydrol 608:127607. https://doi.org/10.1016/j.jhydrol.2022.127607
Shen SL, Xu YS (2011) Numerical evaluation of land subsidence induced by groundwater pumping in Shanghai. Can Geotech J 48:1378–1392. https://doi.org/10.1139/t11-049
Xu YS, Ma L, Du YJ, Shen SL (2012) Analysis of urbanisation-induced land subsidence in Shanghai. Nat Hazards 63:1255–1267. https://doi.org/10.1007/s11069-012-0220-7
Du Z, Ge L, Ng AHM, Zhu Q, Zhang Q, Kuang J, Dong Y (2019) Long-term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors. Land Degrad Dev 30:1785–1801. https://doi.org/10.1002/ldr.3347
Li Z, Cao Y, Wei J, Duan M, Wu L, Hou J, Zhu J (2019) Time-series InSAR ground deformation monitoring: Atmospheric delay modeling and estimating. Earth Sci Rev 192:258–284. https://doi.org/10.1016/j.earscirev.2019.03.008
Zhu L, Gong H, Li X, Wang R, Chen B, Dai Z, Teatini P (2015) Land subsidence due to groundwater withdrawal in the northern Beijing plain, China. Eng Geol 193:243–255. https://doi.org/10.1016/j.enggeo.2015.04.020
Shi L, Gong H, Chen B, Zhou C (2020) Land subsidence prediction induced by multiple factors using machine learning method. Remote Sensing 12:4044. https://doi.org/10.3390/rs12244044
Xu YS, Yuan Y, Shen SL, Yin ZY, Wu HN, MaL, (2015) Investigation into subsidence hazards due to groundwater pumping from Aquifer II in Changzhou, China. Nat Hazards 78:281–296. https://doi.org/10.1007/s11069-015-1714-x
Lin SS, Shen SL, Zhou A, Zhang N (2021) Ensemble model for risk status evaluation of excavation system. Autom Construction 132:103943. https://doi.org/10.1016/j.autcon.2021.103943
Lin SS, Zhang N, Zhou A, Shen SL (2022) Risk evaluation of excavation based on fuzzy decision-making model. Autom Construction 136:104143. https://doi.org/10.1016/j.autcon.2022.104143
Lyu HM, Shen SL, Zhou AN, Yin ZY (2022) Assessment of safety status of shield tunnelling using operational parameters with enhanced SPA. Tunnel Underground Space Technol 123(2022):104428. https://doi.org/10.1016/j.tust.2022.104428
Kanayim T, Moldobekov B (2022) Monitoring and prediction of natural disasters in Kyrgyzstan. United Nations, Office for Outer Space Affairs UN-SPIDER Knowledge Portal. https://un-spider.org/book/5138/monitoring-and-prediction-natural-disasters-kyrgyzstan. Accessed 29 Nov 2022
Huang JY, Zhou QM, Wang FL (2015) Mapping the landslide susceptibility in Lantau Island, Hong Kong, by frequency ratio and logistic regression model. Ann GIS 21(3):191–208. https://doi.org/10.1080/19475683.2014.992373
Erdik M (2017) Earthquake risk assessment. Bull Earthq Eng 15:5055–5092. https://doi.org/10.1007/s10518-017-0235-2
Shen SL, Lin SS, Zhou A (2023) A cloud model-based approach for risk analysis of excavation system. Reliab Eng Syst Saf 231:108984. https://doi.org/10.1016/j.ress.2022.108984
AtanganaNjock PG, Zhou A, Lin SS (2022) A VIKOR-based approach to evaluate river contamination risks caused by wastewater treatment plant discharges. Water Res 226:119288. https://doi.org/10.1016/j.watres.2022.119288
Lin SS, Shen SL, Zhou A (2022) Energy sources evaluation based on multi-criteria decision support approach in China. Sustain Horizons 2:100017. https://doi.org/10.1016/j.horiz.2022.100017
Lyu HM, Xu YS, Cheng WC, Arulrajah A (2018) Flooding hazards across southern China and prospective sustainability measures. Sustainability 10:1682. https://doi.org/10.3390/su10051682
AtanganaNjock PG, Zhou A, Shen SL (2023) Characterization of likelihood of gas hydrates occurrence in South China Sea based on Bonferroni mean-based TOPSIS and fuzzy set theory. Geosci Front 14(2):101513. https://doi.org/10.1016/j.gsf.2022.101513
Sun R, Gao G, Gong Z et al (2020) A review of risk analysis methods for natural disasters. Nat Hazards 100:571–593. https://doi.org/10.1007/s11069-019-03826-7
Arribas A, Fairgrieve R, Dhu T et al (2022) Climate risk assessment needs urgent improvement. Nat Commun 13:4326. https://doi.org/10.1038/s41467-022-31979-w
Kuglitsch MM, Pelivan I, Ceola S et al (2022) Facilitating adoption of AI in natural disaster management through collaboration. Nat Comm 13:1579. https://doi.org/10.1038/s41467-022-29285-6
Elbaz K, Yan T, Zhou A, Shen SL (2022) Deep learning analysis for energy consumption of shield tunneling machine drive system. Tunnel Underground Space Technol. 123:104405. https://doi.org/10.1016/j.tust.2022.104405
Yan T, Shen SL, Zhou A, Lyu HM (2021) Construction efficiency during shield tunnelling through soft deposit in Tianjin China. Tunnel Underground Space Technol 112:103917. https://doi.org/10.1016/j.tust.2020.103917
Yan T, Shen SL, Zhou A, Chen XS (2022) Prediction of geological characteristics from shield operational parameters using integrating grid search and K-fold cross validation into stacking classification algorithm. J Rock Mech Geotech Eng 14:1292–1303. https://doi.org/10.1016/j.jrmge.2022.03.002
Yan T, Shen SL, Zhou A (2022) Identification of geological characteristics from construction parameters during shield tunnelling. Acta Geotech 18(1):535–551. https://doi.org/10.1007/s11440-022-01590-w
Zhang N, Shen SL, Zhou A, Jin YF (2021) Application of LSTM approach for modelling stress-strain behavior of soil. Appl Soft Comput 100:106959. https://doi.org/10.1016/j.asoc.2020.106959
Lin SS, Shen SL, Zhou A (2022) Real-time analysis and prediction of torque of shield cutter head using optimized gated recurrent unit neural network. J Rock Mechan Geotech Eng 14(4):1232–1240. https://doi.org/10.1016/j.jrmge.2022.06.006
Shen SL, AtanganaNjock PG, Zhou A, Lyu HM (2021) Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning. Acta Geotech 16(1):303–315. https://doi.org/10.1007/s11440-020-01005-8
Shen SL, Elbaz K, Shaban WM, Zhou A (2022) Real-time prediction of shield moving trajectory during tunnelling. Acta Geotech 17(4):1533–1549. https://doi.org/10.1007/s11440-022-01461-4
Shen SL, Zhang N, Zhou A, Yin ZY (2022b) Enhancement of neural networks with an alternative activation function tanhLU. Expert Systems with Applications 199:117181. https://doi.org/10.1016/j.eswa.2022.117181
AtanganaNjock PG, Zhang N, Zhou A (2022) Evaluation of lateral displacement induced by jet grouting using improved random forest. Geotech Geol Eng 40:11. https://doi.org/10.1007/s10706-022-02270-y
Zhang N, Shen SL, Zhou A (2023) A new index for cutter life evaluation and ensemble model for prediction of cutter wear. Tunnel Underground Space Technol 131:104830. https://doi.org/10.1016/j.tust.2022.104830
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The conception of the review article was primarily contributed by Jinchun Chai, who also drafted the manuscript and performed critical revisions. Hao-Ze Wu was responsible for the extensive literature search and data analysis. Hao-Ze Wu also contributed to reviewing and editing the manuscript. Both authors have read and approved the final version of the manuscript.
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Chai, J., Wu, HZ. Prevention/mitigation of natural disasters in urban areas. Smart Constr. Sustain. Cities 1, 4 (2023). https://doi.org/10.1007/s44268-023-00002-6
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DOI: https://doi.org/10.1007/s44268-023-00002-6