练继建, 徐梓曜, 宾零陵, 徐奎, CHAN Hoi Yi. 基于Agent的水资源管理模型研究进展[J]. 水科学进展, 2019, 30(2): 282-293. DOI: 10.14042/j.cnki.32.1309.2019.02.013
引用本文: 练继建, 徐梓曜, 宾零陵, 徐奎, CHAN Hoi Yi. 基于Agent的水资源管理模型研究进展[J]. 水科学进展, 2019, 30(2): 282-293. DOI: 10.14042/j.cnki.32.1309.2019.02.013
LIAN Jijian, XU Ziyao, BIN Lingling, XU Kui, CHAN Hoi Yi. Progress of Agent-based modeling for water resources management:a review[J]. Advances in Water Science, 2019, 30(2): 282-293. DOI: 10.14042/j.cnki.32.1309.2019.02.013
Citation: LIAN Jijian, XU Ziyao, BIN Lingling, XU Kui, CHAN Hoi Yi. Progress of Agent-based modeling for water resources management:a review[J]. Advances in Water Science, 2019, 30(2): 282-293. DOI: 10.14042/j.cnki.32.1309.2019.02.013

基于Agent的水资源管理模型研究进展

Progress of Agent-based modeling for water resources management:a review

  • 摘要: 基于Agent的模型(Agent-based models,ABM)研究已成为水资源管理研究理论与方法的重要补充。对水资源管理ABM研究进行归纳与展望,有助于探索优化中国水资源管理体制和机制。在阐述水资源管理ABM概念及内涵的基础上,提炼出主体决策规则和互作机制两个建模核心内容,并对其方法进行了归纳分析;从流域水资源优化配置、城镇居民用水管理和灌区水资源管理3个方面,对2009—2018年主要水资源管理ABM研究进行了综述;针对当前研究的难点与不足,提出未来研究重点:① 拓展复杂适应理论在水资源管理领域的应用;② 加强不确定性水资源管理ABM研究;③ 探索基于机器学习的决策规则建模方法;④ 重视参数校准和结果校验及检验方法;⑤ 加强模型表述格式标准化进程;⑥ 综合权衡水资源管理ABM框架。

     

    Abstract: Agent-based modeling (ABM) has enriched the theory and methods of research in water resources management. Better understanding of the state-of-the-art of ABM and its potential in the field of water resources management can promote institutional development and reform in China's water resources management system. In this paper,while ABM of water resources management (WR-ABM) is defined,its key components—agent decision rules and agent interactions—are identified and their modeling approaches are summarized. Various WR-ABMs applied in basin-scale optimal water allocation,urban household water use and agricultural water management published during the period of 2009—2018 are carefully reviewed. Future research on the use of WR-ABMs that should address the challenges and weakness in the water resources management are discussed and several research directions are recommended herein:① further expanding the use of complex adaptive system theory in the field of water resources management;② coupling ABM and water resources system models that include uncertainties;③ exploring the use of machine learning algorithms in the decision-making modeling;④ improving the methods used in the model parameter calibration,result verification and validation;⑤ using standard documentation protocol,such as the ODD protocol,for the description of models;and ⑥ achieving comprehensive and optimal balance between completeness and simplification in model design.

     

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