Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks

被引:74
作者
Malekmohammadi, Bahram [1 ]
Kerachian, Reza [1 ]
Zahraie, Banafsheh [1 ]
机构
[1] Univ Tehran, Fac Civil Engn, Ctr Excellence Infrastruct Engn & Management, Tehran, Iran
基金
美国国家科学基金会;
关键词
Bayesian Networks (BNs); Reservoir operating rules; Varying chromosome Length Genetic Algorithm (VLGA); Long-term and short-term operation optimization; GENETIC ALGORITHM; MANAGEMENT; OPTIMIZATION; PERFORMANCE; DESIGN; MODELS;
D O I
10.1016/j.envsoft.2009.06.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, a Bayesian Network (BN) is utilized for developing monthly operating rules for a cascade system of reservoirs which is mainly aimed to control floods and supply irrigation needs. BN is trained and verified using the results of a reservoir operation optimization model, which optimizes monthly releases from cascade reservoirs. The inputs of the BN are monthly inflows, reservoir storages at the beginning of the month, and downstream water demands. The trained BN provides the probability distribution functions of reservoirs' releases for each set of input data. The long-term optimization model in monthly scale is formulated to minimize the expected flood and agricultural water deficit damages. The optimization model is developed using an extended version of the Varying chromosome Length Genetic Algorithm (VLGA-II). To incorporate reservoir preparedness for controlling the probable floods in each month, damages associated with floods with different return periods have been considered in the optimization model. For this purpose, a short-term optimization model which provides the optimal hourly releases during floods is utilized and linked to a flood damage estimation model. Damages due to deficit in supplying agricultural water demands are also calculated based on the functions of crop yield responses to deficit irrigation. The developed models are applied to the cascade system of the Dez and Bakhtiari Reservoirs in Southwest of Iran. The result of the trained BN is compared with the rules developed using classical and fuzzy linear regressions and it is shown that the total damage obtained by the BN-based operating rules is about 60 percent less than the total damage obtained using the fuzzy and classical regression analyses. The average relative error in estimating optimal releases is also reduced about 30 percent by using the BN-based rules. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1420 / 1432
页数:13
相关论文
共 42 条
[1]  
[Anonymous], 1996, An introduction to Bayesian networks
[2]   Application of belief networks to water management studies [J].
Batchelor, C ;
Cain, J .
AGRICULTURAL WATER MANAGEMENT, 1999, 40 (01) :51-57
[3]   A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis [J].
Borsuk, ME ;
Stow, CA ;
Reckhow, KH .
ECOLOGICAL MODELLING, 2004, 173 (2-3) :219-239
[4]   The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning [J].
Bromley, J ;
Jackson, NA ;
Clymer, OJ ;
Giacomello, AM ;
Jensen, FV .
ENVIRONMENTAL MODELLING & SOFTWARE, 2005, 20 (02) :231-242
[5]   A guide to the literature on learning probabilistic networks from data [J].
Buntine, W .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 1996, 8 (02) :195-210
[6]   Coupling real-time control and socio-economic issues in participatory river basin planning [J].
Castelletti, A. ;
Soncini-Sessa, R. .
ENVIRONMENTAL MODELLING & SOFTWARE, 2007, 22 (08) :1114-1128
[7]   Bayesian Networks and participatory modelling in water resource management [J].
Castelletti, A. ;
Soncini-Sessa, R. .
ENVIRONMENTAL MODELLING & SOFTWARE, 2007, 22 (08) :1075-1088
[8]   Water reservoir control under economic, social and environmental constraints [J].
Castelletti, Andrea ;
Pianosi, Francesca ;
Soncini-Sessa, Rodolfo .
AUTOMATICA, 2008, 44 (06) :1595-1607
[9]   Bayesian networks in water resource modelling and management [J].
Castelletti, Andrea ;
Soncini-Sessa, Rodolfo .
ENVIRONMENTAL MODELLING & SOFTWARE, 2007, 22 (08) :1073-1074
[10]  
Castillo E., 1997, Expert Systems and Probabilistic Network Models, V493, P543