Intelligent regional water resources management and fallow decision-making system
Date Issued
2011
Date
2011
Author(s)
Wang, Kuo-Wei
Abstract
Water resources management, such as long-term optimal reservoir operation or water allocation, is still a difficult task for decision makers. Complicated systems with high dimensional variables such as long-term reservoir operation usually prevent methods from reaching optimal solutions. In addition, the tolerance of water shortage for irrigation use is higher than that of other water uses such as domestic and industrial uses during drought periods. Therefore, in drought periods, the saved water from fallow areas requires to be reallocated for meeting the water demands of public use. This study proposes a variable decomposition strategy and a fallow management system for long-term reservoir operation and fallow management, respectively.
First, a multi-tier interactive genetic algorithm (MIGA) is proposed in this study which decomposes a complicated system (long series) into several small-scale sub-systems (sub-series) where Genetic Algorithm (GA) is applied to each sub-system and the multi-tier (key) information mutually interacts among individual sub-systems so that obtain the optimal solution to long-term reservoir operation. The Shihmen Reservoir in Taiwan is used as a case study. In addition, three long-term operation cases are implemented with the MIGA search, a sole GA search and a simulation based on the M-5 rule curves (the operation guideline of the Shihmen Reservoir) for comparison purpose. The improvement rate of fitness values for MIGA over the M-5 rule curves increases more than 15%. The improvement rate of fitness values for MIGA over sole GA increases more than 25%, and the computation time dramatically decreases 80% in a 20-year long-term operation case. Results demonstrate that MIGA is far more efficient than the sole GA and can successfully and efficiently increase the possibility of achieving an optimal solution.
Second, in the catchment of northern Taiwan, water scarcity becomes more and more serious and water stress and a deduction of water supply to irrigation use due to climatic change becomes major problems as well. This study proposes a framework of strategic pre-fallow decision making processes and addresses three important fallow issues: (1) the threshold of water shortage; (2) the appropriate fallow ratio; and (3) the timing for publicizing the fallow ratio. The first crop of paddy in northern Taiwan is used as a case study. A great number of system simulations based on current reservoir storage and upcoming three-month inflows are used to provide the preliminary judgment of water shortage and then to assign the drought threshold. After the drought situation is substantiated, a neuro-fuzzy network is used to determine the suitable fallow ratio and provide both water shortage information and the impacts of restrictions on irrigation water. Finally, the timing to publicize the fallow ratio is determined through the analysis of current M-5 operation rule curves simulation and genetic algorithms (GAs) search. Results demonstrate that the proposed intelligent fallow decision-making system can be a very useful tool and beneficial for the sustainable use of water resources. Drought thresholds of reservoir storage should be Q70 to Q60 (slight drought) and Q90 to Q80 (severe drought), respectively, when considering an early fallow strategy (Case I) and a late fallow strategy (Case II). The earlier the timing to publicize the fallow ratio is, the less the amount of fallow compensation is (approximately 22% less in this case study).
In sum, the problems of long-term reservoir operation can be successfully solved by MIGA and decision-makers can refer to the decision-making system for fallow strategies proposed in this study when allocating water resources during drought periods.
Subjects
Optimization
Reservoir operation
Decomposition
Fallow
Decision-making
Multi-tier interactive genetic algorithms (MIGA)
Adaptive neuro-fuzzy inference system (ANFIS)
SDGs
Type
thesis
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