A Study on Water Stage Increment of the River due to Reservoir Drainage by Artificial Neural Network

• Main Authors: Da-Yuan Chang, 張大元
• Other Authors: An-Pei Wang
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/20190379759396040336

碩士 === 中原大學 === 土木工程研究所 === 92 === Abstract In Taiwan, roughly 78% of its yearly rainfall concentrates in the summer and autumn because of the particular climate and geographic characteristics. During Typhoon period, the reservoir operators often face the dilemma of maintaining more floodwater and taking the risk of failure of the dam and taking the risk of being drought if excess floodwater is released. The most difficult task of reservoir operation is to consider all the functions of the reservoir. To achieve this purpose, forecasting the inflows of reservoir and simulating downstream water-stage due to the drainage of reservoir are essential to operators. Watershed of Shihmen reservoir and Da-han River are taken as demonstrations in this paper. The Artificial Neural Network (ANN) simulates the characteristics of river basin, and a “rainfall-runoff” model is established. While the model is built, we could predict the inflows of several hours later. In addition, the relationship between drainage and water-stage is found. Some important results and a three-dimension plot of drainage, water-stage and tide are present in this paper. The plot could figure out relationships between drainage and water-stage under different rainfall intensity or tide-level conditions. It is expected that this research be used for online reservoir operation in the future.