Development of Optimization and Simulation Models for Conjunctive Use of Surface Water and Groundwater
|Keywords:||水資源利用;地面地下水聯合運用;優選模式;模擬模式;類神經網路;Water resource utilization;Conjunctive use of surface water and groundwater;Optimization model;Simulation model;Artificial Neural Networks||Issue Date:||2009||Abstract:||
Efficient utilization of water resources is one of the most concerned issues for countries and regions worldwide. In Taiwan due to soaring economy growth after 1970s, the water consumption by domestic and industrial sectors increased dramatically. As the frequency of water shortage increased, the rational allocation of water among utilization sectors becomes a disputed problem year after year. The agriculture consumes nearly 70% of total water consumption, and even approaching 90% in central Taiwan. There has been a significant pressure of water demand from industry development over the past few years in central region. Accordingly, the capability of transferring agricultural water to support other sectors worth further examination. This paper explores the conjunctive use of surface water and groundwater by means of simulation-optimization model theory, and in accordance with the optimal hydrograph of surface water and groundwater applying Artificial Neural Networks to predict future variation of groundwater level. It is expected to raise advantage of water resource utilization and serve as reference for development planning in that region.he study case focused to the alluvial fan area of Cho-Shui Creek（Choshuishi Alluvial Fan, Taiwan）where kept most affluent reserve of surface water and groundwater. Diverse schemes of conjunctive use were based on hydrological records of last 47 years. Four simulation scenarios were composed that considering the inclusion and exclusion of the pump amount outside the irrigation area of Irrigation Association and the future industrial volume of Central Taiwan Science Park. The results pointed out that, with exclusion of pump outside the irrigation area of Irrigation Association and exclusion of future industrial use of Central Taiwan Science Park, demonstrates the lowest annual irrigation shortage rate (SR) of 1.48%, with shortage index (SI) 0.1446 per year and 0.6003 per 10-day period respectively, which is not a water scarcity problem in regard to agriculture sector. However, the scenario with inclusion of pump discharge outside the irrigation area of Irrigation Association and inclusion of industrial water use of Central Taiwan Science Park would reach a highest SR of 14.97%, and a SI of 3.3913 per year and 9.0768 per 10-day period respectively, which showed the existence of water deficit situation. Furthermore, in model-analyzing and formulation, industrial water took a less proportion than agricultural water. Under water shortage of surface water or groundwater in Changhua district, the water shortage situation increased significantly, with 10-day SI increases from 3.8308 to 23.0245.rediction of groundwater level in Alluvial Fan area of Cho-Shui Creek has been applied the Artificial Neural Network algorithms, including BPNN, RBFNN and RNN. The research result revealed that Artificial Neural Networks could give estimation of water table variation in the coming month at seven groundwater monitoring stations in Changhua and Yunlin district, and the RBFNN and BPNN algorithms showed the best performances. The prediction of groundwater level in 30 days, the 40 cm difference was within reasonable range. Findings of the research provide valuable source of information of conjunctive use of surface water and groundwater, groundwater level monitoring, utilization and control of groundwater.
|Appears in Collections:||土木工程學系|
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