Intelligent Water Resources Allocation Strategy for Growing Water Demands

• Main Authors: Yu-Chung Wang, 王昱中
• Other Authors: Fi-John Chang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/84465404129506403879

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 102 === The population growth and economic development in Taiwan has led to a tremendous demand for natural water resources. In recent years, water shortage problems frequently occur in northern Taiwan such that water is usually transferred from irrigation sectors to public sectors during drought periods. In response to the uneven spatio-temporal distribution of water resources and the problems of increasing water shortages in this region, it is a primary and critical issue to simultaneously satisfy multiple water objectives through adequate reservoir operations for integrated water resources management. For sustaining water resources and agricultural development, this study intends to build up the optimal agricultural water resources allocation strategies adapting to the growing water demands of both agricultural and public sectors. The optimal allocation strategies are expected to adequately suggest quarterly water shortage indexes for the period of the first paddy crop such that early assessment and decisions on water allocation can be made for drought mitigation management. The Shihmen Reservoir in northern Taiwan is used as a case study. According to previous studies and historical multi-objective reservoir operation data of the Shihmen Reservoir, this study investigates the changes in water supply targets and design possible nine water demand conditions that may occur in the future. Based on these designed conditions, we use a system analysis approach to conducting the simulation (based on M-5 rules) and optimization search (by the non-dominated sorting genetic algorithm-II (NSGA-II)) of the reservoir operation sequences. The results indicate that the NSGA-II method can search the optimal water allocation series meeting the objectives subject to restrictions and producing a lower water shortage index. It demonstrates that the NSGA-II produces good performance for reservoir operation problems. Artificial neural networks (ANNs) have the ability to learn and deal with complex problems and uncertainty issues. In this study, the back-propagation neural network (BPNN) and the adaptive network fuzzy inference system (ANFIS) are used to estimate quarterly water shortage indexes in drought periods for agricultural and public sectors based on the selected input factors and the determined thresholds. The shortage index obtained from the NSGA-II is the training target of the output layers for both ANN models. The results indicate that the BPNN and the ANFIS models have equally good performance in estimating the shortage indexes for both sectors, but the ANFIS model produces better performance in stability. This proposed approach can be used as an early warning system for drought mitigation, which can become a reference guideline for sustainable water resources management.