Applying Artificial Neural Network on the Optimal Flood Control and Sediment Sluicing Model for Shi-Men Reservoir

• Main Authors: Su, Jun-Lin, 蘇俊霖
• Other Authors: Chang, Liang-Cheng
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/44991743284746227597

碩士 === 國立交通大學 === 土木工程系所 === 105 === In Taiwan, frequent typhoon events result in flood related damages such as reservoir sedimentation, dam failure, and uncertain water supply. Therefore, this study develops a model to optimize the reservoir operation to control the flooding damage, increase the water supply, and improve the sediment sluicing efficiency. An optimal flood control model is developed using Genetic algorithms, a river simulation model, and an Artificial Neural Network (ANN) model. This developed model has multiple objectives including flood control, water supply, and sediment sluicing. Shimen Reservoir is selected for this study. Three historical typhoon events are used: Typhoon Jangmi, Typhoon Fung-Wong, and Typhoon Sinlaku. The results show extraordinary operation efficiency improvement in terms of flooding control and sediment sluicing. This model increases the sluicing sediment efficiency for 36%, or 174,000 tons; 44%, or 118,000 tons; and 54%, or 96300 tons for Typhoon Jangmi, Typhoon Fung-Wong, and Typhoon Sinlaku respectively. These result shows that the developed model can be a very useful tool for optimal flood control operation for reservoirs.