A Study of Applying ANN on Rainfall-Runoff Model

• Main Authors: Wei-Lun Zhang, 張維倫
• Other Authors: I Tsou
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/90997274881268256236

碩士 === 國立屏東科技大學 === 土木工程系所 === 100 === In hydrological applications, Empirical formula is conceptually simple, easy to use, commonly used in the compute relationship between Rainfall and Runoff . Particularly to no flow record region or engineering design, through the few parameters such formula can quickly estiniate a peak flow discharge and lag time. Artificial neural network (ANN) is one of a commonly used black-box model scheme with variables mathematical structure and can objectively judge the nonlinear relationship between input and output data. In recent years, many studies pointed out that the neural network can derived successfully the mapping relationship between ramfall and runoff. This study tries to applied ANN model on Linbian Creek rainfall runoff analysis. The model input data include different rainfall periods and amount in proportion to 0-25%, 25%-50%, 50%-75%, and 75%-100% stage. By using these input data, model will estimate the hydrograph parameters, such as peak discharge, base time, time to peal, time to 50% and 75% peak, and width of time of 50% and 75% peak. The final model verified by 4 flood events, and results compared to traditional multiple regression model and Snyder unit hydrograph showed that ANN model has more accurate estimation than other two methods on most of parameters. Therefore the future practical engineering application, only need thedesign hyetograph, as the input of the neural network model ehich can estimate the runoff discharge hydrograph fo the tiver baisn, hydrograph in Linbian Creek.