Remote Sensing Imagery for Typhoon Rainfall Forecasting ─ ANN Approach

• Main Authors: Meng-Jung Tsai, 蔡孟蓉
• Other Authors: 張斐章
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/80625957357617410897

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 94 === Rainfall forecast is very important for improving the efficient management of water resources systems. Nevertheless, accurate rainfall forecasting is still a great challenge faced by hydrologists. In this study, a station-based rainfall forecast model is constructed to forecast one-hour-ahead rainfall values during typhoon events. The developed model is constructed based on artificial neural networks (ANN) techniques which are capable of handle complex and non-linear systems. The available data are constituted by hourly rainfall values from 23 different events observed at the DanShui observation station and GMS-5 remote sensed data collected during 2000 to 2004. Firstly, to investigate the influence of the input information, three different schemes (schemes I, II and III) are proposed based on hourly rainfall, characteristics of typhoon and GMS-5 remote sensed data , respectively, and then applied to two different models, backpropagation neural network (BPNN) and multiple regression method (MRM) . The results showed that the BPNN model with scheme III, which includes nine cloud-top-temperatures of three thermal infrared and hourly rainfall measured data, presented the best performance. Furthermore, we have processed the input data reduction by two methods, (a) the average method and (b) the principal component analysis, and investigated their effectiveness through by three models-BPNN, MRM and Radial Basis Function Neural Network (RBFNN). The results suggest that the RBFNN model with input data reduction by the principle component analysis presented the best performance with smallest root mean square error (RMSE=4.74mm) and highest correlation coefficient (CC=0.51) when compared to all investigated schemes and forecast models.