Keywords

1 Introduction

In Ecuador there are data from conventional stations approximately since 1990 as recorded on the page of the Meteorological Service of Ecuador [1]. These can be taken by meteorological observers who take the information to the nearest offices to each station or send the notebooks by postal service. A conventional station is a mechanical device used to take measurements of meteorological variables based on the instruments used to perform the measurements. As mentioned in the literature [2] there are three types of stations: the main ones that perform 5 daily observations with a minimum of 9 variables; the secondary ones that realize 3 daily observations with a minimum of 3 variables and the pluviometric ones that make observations once a day. Additional data records are observed at a national level and are monitored by automatic stations.

A meteorological record is an observation of an atmospheric variable, which plays a key role in many applications of flood, drought, environment and water resources. Although rainfall observations are the most used, other parameters of interest include air temperature, humidity and wind speed [3].

The Agrarian University of Ecuador maintains to date two meteorological stations, in Guayaquil and in Milagro. Each station measures values of temperature, humidity, rain, wind speed and direction as well as barometric pressure. The stations are kept within the premises of the University and transmit the records automatically on the basis that it is much more economical and sustainable to maintain an automatic station and obtain remote data from it considering that hiring staff who perform meteorological observations daily in conventional stations leads to higher costs as well as different types of obligations on the part of employers [4].

Ecuador, given its geographical position, is located in the intertropical zone, where the presence of the Andes mountains, the influence of the Pacific Ocean and the Amazonian forest, have formed diverse climatic floors and a great variety of subclimates, microclimates and topoclimates that go from the tropical to the naval [5].

With these climatic characteristics, it is important to have a system that allows in real time, to know parameters that help the common citizen to make quick decisions regarding the most suitable activities that can be done at a particular time and location, or historical information of what has happened to the comfort level of the environment, which can be known with the service explained in this document.

Guayaquil is the largest city in Ecuador with approximately 2.6 million inhabitants in the metropolitan area. The city is located at the sea level (2° 12’ S and 79° 54’ W). Due to the marshes in the west and the river in the east, the city has grown mainly towards the north and towards the south [6]. The aforementioned station is located in the south of the City in the University area.

Guayaquil has a warm and humid climate because it is part of the coastal zone of Ecuador. There are two distinct seasons: the rainy season from December to April and the dry season from May to November. The precipitation is limited to 4–5 months, the humidity remains high all year due to the proximity to the Pacific Ocean. The weather is very stable during the year despite the high temperatures. The wind speed is low, while the solar radiation is quite strong during the whole year. The rainy season has the worst thermal conditions, since both the air temperature and the vapor pressure are higher and the wind speed is lower.

With a great pre-Hispanic culture, the city of Milagro, where the Ciudad Universitaria Milagro (CUM) is located and has the second meteorological station, is placed further northwest (2° 8’ S and 79° 35’ O), further away from the sea on alluvial banks of the Milagro River at an average height of 11 m asl. The city is surrounded by a large agro-industrial activity of old date, conforming the sugar center of Ecuador [7].

The Tropical climate is thermal and humid. It characterizes the Canton Milagro, including the city Milagro, with a range of average daily temperatures of 25 to 27 °C and average annual rainfall of 1100 to 1800 mm, with a rainy period of 120 days to the west and 150 to the east, between the months of January to May, which favors agriculture. Likewise, other crops are favored in areas with hydric deficit of 400 to 600 mm and potential evapotranspiration of 1400 to 1500 mm [8].

According to different studies, Open Geospatial Consortium [9] information management services standards (WCS- Web Coverage Service, WFS- Web Feature Service and WMS- Web Map Service) are managed, which provide customizable visualization and access to both geospatial coverage and data of the features in a standard and simple way.

The purpose of this article is to review different tools for WMS services, taking into account that geospatial data are valuable for research in many areas.

The development of an approach based on sensors that are monitored through the web to derive typical information offered by a dynamic web mapping service (WMS) is described. The system allows easy and convenient synergistic research in a virtual platform for professionals from different areas and the general public, which greatly encourages the exchange of global data and collaboration to scientific research.

This study is based on web technology, generating a distributed architecture, which allows to easily add new nodes, computing and data to the storage system, providing a solid computing infrastructure for regional climate change.

2 Related Studies

Currently, scientists, researchers and developers have integrated efforts for the creation of information systems that allow the integration of meteorological stations with sensors installed in them. They also obtain access to geospatial data using different tools and models that allow decision-making based on the presented results. Webmapping technology is a widely used concept. It refers to the interactive process of designing, applying and generating geospatial data through the WWW, using a GML format (Geo-graphic Markup Language) [10] based on the specifications of OpenGIS Con- sortium [11].

In this context several efforts are made for the creation of such applications that allow the generation and diffusion of different types of geospatial data and the generation of maps on the web. So in [12] an interoperable framework is presented which generates images that can be accessed through the GIS software for different applications based on Earth science and the Web. The access is possible by the compliance of the Web Map Service (WMS) of OpenGeospatial Consortium for interoperability in such a way that any WMS viewer can access the service. Its main function is to use a series of interoperable services to support analysis of natural hazards, such as flood forecasts, real-time routing and support for other environmental decision-making applications, as well as disseminate various types of spatio-temporal data of Earth science.

On the other hand, [13] proposes an independent system to monitor the climate based on the IoT (Internet in things), which considers the use of the minicomputer with a low-cost ARM structure such as Raspberry Pi, in addition to using an external Wi-Fi module, for data processing. The system has been developed in Python. The information can be monitored from terminals such as laptops, smartphones and tablets that have easy access to the Internet. The information is provided in real time and includes parameters such as temperature, humidity, pressure, CO and harmful air pollutants. The system helps the sustainable growth of the city and improves the lives of citizens. The ubiquitous availability of dynamic datasheets in the dashboard and timely graphical representation can help plan control measures against rising pollution levels and raise people’s awareness.

Also in [14] a WebGIS observatory platform is presented. It is designed for risk evaluation, preparation and response to emergencies in coastal areas. This tool combines a sophisticated prognostic modeling system for water map analysis including wave prediction, hydrodynamics and oil spills, with real-time monitoring networks with continuous validation. This system has been customized for the assessment of the risk of oil spills and the rapid response to an oil spill emergency. The authors seek to assess risks through georeferenced maps and layers of GIS information, in order to visualize predictions through georeferences.

There is an approach based on web sensors created by [15], which developed a prototype that provides daily maps of the productivity of the vegetation of the Netherlands with a spatial resolution of 250 m. The MODIS (Moderate Resolution Imaging Spectroradiometer) [16] surface reflectance products are daily available and the meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model and they implement the automated processing facility.

In the literature there is also the development of systems for the analysis of geospatial data, such as the one presented in the article “Web-based Visualization Platform for Geospatial Data” [17], whose main objective is to explore new ways of visualizing and interacting with multidimensional satellite data and computed models of several Earth observations. This new V-MANIP platform facilitates a multidimensional exploration approach that allows to see the same data set in multiple viewers at the same time to search and explore efficient interesting features within the displayed data.

In general, the scheme of systems architectures based on geospatial web services has been presented in several investigations as an integral part of a virtual research environment (VRE) for statistical processing and the visualization of meteorological data and climate data. Thus, [18] presents an architecture consisting of a set of independent SDI nodes interconnected with corresponding data storage systems. Each node runs specialized software such as a geoportal, cartographic web services (WMS/WFS), a catalog of metadata and a MySQL database of technical metadata that describe geospatial datasets available for the node. It also contains geospatial data processing (WPS) services based on a modular compute backend perform statistical in order to process functionality and, therefore, provide analysis of large data sets with visualization, exporting results in standard format files (XML, binary, etc.). Some of the cartographic web services have been developed in a prototype system to provide capabilities to work with raster geospatial data and vectors based on OGC web services.

3 Evaluation of Tools to Enable WMS Service

3.1 Assessment of the Evaluation Criteria for WMS Tools

The project “Platform for monitoring real-time atmospheric data of the network of meteorological stations of the Agrarian University of Ecuador, Guayaquil and Milagro headquarters”, suggests to incorporate different ways of representing monitored meteorological variables. One of the proposed ways is the implementation of the WMS service with an interface that helps with the spatial referencing of the results or analysis of meteorological data.

Through an evaluation of the tools for application of WMS services that currently exist, different criteria were established that are considered important within the implementation process and the presentation of results. For this purpose, 22 tools were analyzed, identifying 3 Factors of Analysis: (1) Form of Access, (2) Functional Requirements, (3) Presentation of Results [19].

Within the first factor analyzed -tool access- it was assessed how to acquire the 22 tools, whether they are free access with the value of 1, or through a license with the value of 0; finally we had 16 free access tools and 5 licensed tools. To evaluate each of the two remaining factors a survey was conducted with 30 users. The users indicated indicating whether the service is provided by the tool using the Likert scale where 0 means the absence of the service in the tool and 1 represents the presence of the service in the tool.

The second factor that was analyzed is the functional requirements, that is, the easiest and most intuitive way to use each of the tools described above. Four important criteria for the geospatial information survey process were specified at the time of monitoring environmental variables. The 22 tools were evaluated according to the four criteria on a scale of 0 to 1 where zero is the absence of the characteristic in the tool and 1 is the highest score of the characteristic. Within the third factor analyzed, three important criteria were identified for our project at the time of verifying the presentation of the meteorological data analysis. As we did in the functional requirements phase, we valued each tool under the three criteria in a scale from 0 to 1. These ratings are described in Table 1.

Table 1. Evaluation of WMS tools.
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3.2 Identifying the Best WMS Tool

Once the different factors and criteria have been assessed to identify the tool that best fits within the project “Platform for the monitoring of atmospheric data in real time of the network of meteorological stations of the Agrarian University of Ecuador, Guayaquil and Milagro headquarters”, two analysis were carried out. In the first instance, we proceeded with a Cluster analysis of the criteria evaluated in the three factors, since this multivariate statistical technique allows us to group the tools that have the criteria and/or characteristics with the maximum homogeneity and the greatest difference between the groups, with which we obtained a total of 4 groups (cluster) described in Table 2.

Table 2. Analysis of evaluated factors
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Verifying the centroids of each of the clusters, we identify that cluster 2 complies with the required characteristics. All the tools in this group are freely accessible and the average of the criteria for the data presentation factor is the highest of the other 3 groups that meet the first condition. This is described in Table 3.

Table 3. Centroids analysis
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Secondly, with the tools already grouped and the most appropriate group chosen, we gave weight to each factor according to the need and characteristics of our project, leaving 40% of the weight for the Acceptance Form factor, 25% for the functional requirements factor and 35% presentation of results. As we can see in Table 4, when obtaining the final score by applying the weights selected in the factors to each tool, we can identify that the tool that most adjusts to the characteristics of the Project is “Geoserver”, since it is a free access tool and the percentage in the factor of presentation of results fulfills in 97% the weight of this factor. The aforementioned tool would be used in the implementation of the WMS service within the platform that is maintained.

Table 4. Tool analysis by weight
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4 Methodology

This section shows the data monitoring model implemented on the project website. The installed sensors can obtain, access, manage and process environmental data in real time [20]. Therefore, a sensor web service platform is adopted that integrates the technology to provide the interfaces in GSW (Web Services Management) to record, plan and present relevant geostatistical information that supports the realization of the GIS data model in real time, which will be implemented in the future.

4.1 Monitoring Architecture

In general, the monitoring platform maintains a three-level architecture, with a structure designed in such a way that it allows to add meteorological stations to the network. This is shown in Fig. 1.

Fig. 1.
figure 1

Computational structure of the system

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It can be seen in Fig. 1 that with the stations in operation there are currently three levels in the architecture: first level of observation, second level for data interpretation and the third level that involves presentation through the web. The level of observation supports the installed sensors, which measure different environmental parameters in real time (temperature, humidity, precipitation, wind speed and direction), maintaining communication with the web server through remote access, which is a limiting factor to obtain the measurements recorded by the stations currently installed and in operation.

The service of publication and interpretation of data allows the integration of third-party services such as WMS services commonly used within web geoprocessing [21]. This service is currently implemented through the Google maps APIs and encoded in php using Bootstrap.

Within the data interpretation process, Google Earth is used to generate a kml file that is a markup language based on XML to represent geographic data in three dimensions, since this language allows polygons to be added to mark specific sites of geographical areas within of a map.

Figure 2 shows how the polygons were added in shapefile format to represent the Miracle area and the Guayaquil area. Then each polygon was downloaded in KML(Keyhole Markup Language) format.

Fig. 2.
figure 2

Graphic representation of polygons

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Having the KML file with the marked areas, we proceeded to use Google “my maps”, generating a main layer of the map. The pertinent permissions were released for publication and a KMZ (Keyhole Markup Zip) file was downloaded.

With the KMZ file generated, the Api Google circle was used and through Java script the Api is called with the kmz file previously generated to get a map with a marked area of Guayaquil and Milagro. In the coding the circle represents the heat index [22] previously calculated with the following formula:

$$ \begin{aligned} {\text{IC}} & = - 8,78469476 + 1,61139411\cdot{\text{T}} + 2,338548839\cdot{\text{HR}} - \\ & \quad 0,14611605\cdot{\text{T}}\cdot{\text{HR}} - 0,012308094\cdot{\text{T}}2 - 0,016424828\cdot{\text{HR}}2 + + 0,002211732\cdot{\text{T2}}\cdot{\text{R}} \\ & \quad + 0,00072546\cdot{\text{T}}\cdot{\text{HR}}2 - 0,000003582\cdot{\text{T}}2\cdot{\text{HR}}2 \\ \end{aligned} $$

Where IC equals the heat index, T is the air temperature and HR is the relative humidity.

The level of presentation of the information uses a protocol of access to resources and a standard service protocol, respectively, depending on the service that will be used, so in Sect. 5 all the information represented at this level is explained.

5 Environment of the Current Platform of Services Based on Meteorological Data

In the service platform currently implemented, an adaptive design has been used that allows users who visualize the platform to achieve a user experience based on usability.

The web platform of meteorological data-based services receives data from the stations located in the city of Guayaquil and Milagro, which are registered through the datalogger on the server that contains the web application that allows the statistical information about temperature, humidity, pressure, dew point, wind and precipitation. The services offered are detailed in Table 5.

Table 5. Meteorological data services offered by the platform
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The option statistics allows you to select the city (Guayaquil or Milagro) and present the information by day, month or year, as can be seen in Fig. 3. It shows the data of February 12, 2018, corresponding to temperature and humidity.

Fig. 3.
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Comparative of captured data of sensors of temperature and humidity

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The statistical graphs that the application generates -according to the data recorded by each sensor of the meteorological station- calculate the maximum and minimum measurement of the different variables, which are represented by colors.

Within this statistic the information about the speed distributions and frequency of variation of the wind directions is represented, through the graph of the rose of the winds, based on the observations captured by the weather stations of Guayaquil and Miracle. This is observed in Fig. 4.

Fig. 4.
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Speed distributions and variation frequency of the wind directions

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The option of WMS services generates through different colors the graphic representation of the “Thermal Sensation”, which is just the sensation of greater heat or cold that a person feels on their skin when exposed to an environment with certain special conditions of wind or humidity associated with the existing air temperature.

Likewise, through this information, we can manage the systems of climate conditioning of homes, offices and other areas of human activity, which need to be permanently adjusted, generating energy savings and satisfactory comfort indices. On the other hand, the compilation of this type of information can be used by architects and landscapers in the design of buildings, green areas, avenues, etc., providing better characteristics to those spaces for the use and enjoyment of its occupants.

In addition to the information provided and once a more robust network of interconnected stations is established, they will be able to provide information to the public entities responsible for service supply such as energy and drinking water, with which they could adjust tariffs on subsidies granted, with a social vision of support to those with less resources, who generally consume the least.

Last, but not least, we would have the possibility to directly attend the field producers with this information about the thermal sensation, both for their own activity in the daily work, as well as with what is related to planting crops and raising farm animals. They have different sensitivities to weather conditions and spatial - temporal variations.

Information on 23rd February 2018 can be seen in Fig. 5.

Fig. 5.
figure 5

Weather conditions and their spatial-temporal variations

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In the future it is expected to present this information in real time. The platform in turn allows to obtain the data recorded in * cvs file format, entering the range of dates to obtain them. This is done with the option “station data”.

6 Conclusions and Future Research

The main objective of this study was to suggest an evaluation that integrates a data representation model and a sensor web services platform within a geospatial services web framework for the management of environmental data. As a experiment it was carried out a monitoring of different environmental parameters in a range of 1 min, for approximately seven months in two different locations. The preliminary results show that the use of this method to administer environmental data in real time is feasible and effective. The objective of the experiment is to show the proposed model and platform under a GSW framework and its applications for environmental data management.

The future work will focus on analyzing the scientific problems associated with the experimental results, such as margin of error of the stations and implementation of visualization models based on other monitored variables using the GeoServer tool chosen in this work, under a cluster analysis. At the same time, the integration of other sensors such as solar radiation and UV index is suggested.