Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

作     者：Giuliani, Matteo Castelletti, Andrea Pianosi, Francesca Mason, Emanuele Reed, Patrick M. 

作者机构：Politecn Milan Dept Elect Informat & Bioengn Pza Leonardo da Vinci 32 I-20133 Milan Italy ETH Inst Environm Engn Ramistr 101 CH-8092 Zurich Switzerland Univ Bristol Dept Civil Engn Queens BldgUniv Walk Bristol BS8 1TR Avon England Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA 

出 版 物：《JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT》 (水资源规划与管理杂志)

年 卷 期：2016年第142卷第2期

页      面：4015050-1-4015050-17页

核心收录：

学科分类：08[工学] 0815[工学-水利工程] 0813[工学-建筑学] 0814[工学-土木工程] 

基　　金：IMRR-Integrated and sustainable water management of Red-Thai Binh Rivers System in changing climate research project - Italian Ministry of Foreign Affair as part of development cooperation program Natural Environment Research Council (Consortium on Risk in the Environment: Diagnostics, Integration, Benchmarking, Learning and Elicitation (CREDIBLE) [NE/J017450/1] NERC [NE/J017450/1] Funding Source: UKRI 

主　　题：Water management Direct policy search Multiobjective evolutionary algorithm 

摘      要：Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case s relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP. (C) 2015 America