Acta Scientiarum Naturalium Universitatis Pekinensis

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Key Components and Modeling Framework for Intelligent Watershed Management (IWM)

ZOU Rui1,3, LIU Yong2, YAN Xiaopin2, GUO Huaicheng2   

  1. 1. Tetra Tech, Inc. 10306 Eaton Place, Ste 340, Fairfax, VA 22030; 2. College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences MOE, Peking University, Beijing 100871; 3. Kunming Challenger Technology Inc., Kunming 650021;
  • Received:2010-07-01 Online:2011-09-20 Published:2011-09-20



  1. 1. Tetra Tech, Inc. 10306 Eaton Place, Ste 340, Fairfax, VA 22030; 2. 北京大学环境科学与工程学院, 水沙科学教育部重点实验室, 北京 100871; 3. 昆明诚锐环保科技有限公司, 昆明 650021;

Abstract: A new watershed management module, Intelligent Watershed Management (IWM), was proposed. The decision need for watershed management was analyzed before the conception and components of IWM were formulated. The idea of IWM comes from the principles of bionics and imitates the process of human decisions. The IWM is significantly differing from traditional watershed management, which includes three main steps, i.e. watershed information inquiring, input-output response modeling at the watershed scale, and optimized decision making. Four key issues of IWM were identified, including the management goal, suitable models, non-linear responses and computational requirement, and the uncertainties in the watershed system. The modeling framework of IWM was developed and some new algorithms were proposed, such as Risk Explicit Interval Linear Programming (REILP) and Nonlinearity-Interval Mapping Scheme (NIMS) for simulation-optimization analysis. Some cases were demonstrated for the efficiency of the proposed models.

Key words: intelligent watershed management, decision making, simulation-optimization, uncertainty

摘要: 在总结流域问题现状及流域管理决策需求的基础上, 基于仿生学原理, 类比人的思维特征和决策过程, 提出了包括流域信息获取、输入- 输出因果关联模拟以及优化决策等步骤在内的智能流域管理模式, 并对比分析了智能流域管理和传统流域管理的差异性。提出了智能流域管理的4个关键问题: 管理目标的确定、基于智能因子关联与流动的模型开发与信息获取、非线性响应特征与流域决策模型的计算需求、流域系统的不确定性与决策适应性演化, 构建了智能流域管理的模型框架, 并通过案例展示了模型框架中的重要进展与预期决策支撑作用。

关键词: 智能流域管理, 决策, 模拟-优化, 不确定性

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