Abstract
Deforestation, severe floods, and other large-scale natural disasters indicate the major effects of human activities on the environment. Water resources as complex systems are stochastic, dynamic, and continuous. The complexity of water resources can significantly be increased by human interventions. The Agent-Based Models (ABMs) are known as an efficient tool in the modeling of complex systems, especially water resources models. Unique and autonomous decision-making units called agents with exclusive historical memory interact with each other and with their environment mutually in ABMs. Agents could be distinguished by their water demand as well as socioeconomic characters in a spatiotemporal dynamic environment. In the water resources field, agents could be any types of water users e.g. agricultural and industrial users, drinking/household water users, etc. Although the collected result of simple behaviors of agents is complicated, the definition of agents and their relationships are simple. Entities such as organization or individuals, called agents, are defined with simple and stupid rules for acting in an environment to make ABMs. However, aggregated behaviors of agents create complexity of the system. Hence, ABMs will be able to provide a model that resembles the real world. Therefore, policymakers and decision makers can benefit from using such models to better understand the potential outcomes of their decisions in the systems. The bottom-up approaches can be implemented through the Agent-Based Models (ABMs). Such approaches can help policymakers perform more efficient strategies, choose the most profitable scenario, identify potential risks and negative consequences. In this chapter, the Agent-Based Model (ABMs) is introduced to model Coupled Human and Natural Systems (CHANS) especially water resources that emphasize on one of the important principles of the computational intelligence called evolutionary computation.
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Bahrami, N., Sadr, S.M.K., Afshar, A., Afshar, M.H. (2022). Application of Agent Based Models as a Powerful Tool in the Field of Water Resources Management. In: Bozorg-Haddad, O., Zolghadr-Asli, B. (eds) Computational Intelligence for Water and Environmental Sciences. Studies in Computational Intelligence, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-19-2519-1_23
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