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Wang, Y., Zhou, Y., Franz, K., Zhang, X., Ding, K.J., Jia, G. and Yuan, X. (2021) An Agent-Based Framework for High-Resolution Modeling of Domestic Water Use. Resources, Conservation and Recycling, 169, Article ID: 105520.
https://doi.org/10.1016/j.resconrec.2021.105520

has been cited by the following article:

• TITLE: On the Use of Agent Based Modelling for Addressing the Social Component of Urban Water Management in Europe

  AUTHORS: Ifigeneia Koutiva, Christos Makropoulos

  KEYWORDS: Agent-Based Modeling, Urban Water Management

  JOURNAL NAME: Computational Water, Energy, and Environmental Engineering, Vol.10 No.4, August 23, 2021

  ABSTRACT: The paper aimed to provide a review of different tools that estimate how human behavior changes by water management strategies and quantify this change to support the decisions of urban water managers. To support decision makers, it is essential to be able to model the urban water system’s human part explicitly and link it to the hydro system’s response, rather than only explore the reaction of the system based on scenarios. To do so, tools are needed that can model the human part of the system, explore its reaction to potential changes and dynamically link back this to the techno-environmental model of the water system. This work reviews state-of-the-art ABMs that are publicly available focusing on the human part of the urban water system in Europe. The review leads to the proposals of three pillars for future development of ABMs for urban water management in Europe: end-user enablement; Machine Learning and Artificial Intelligence integration and adversaries modelling.