Study on Typhoon Rainfall Forecasting by Using Self-Organization Algorithm Model for Sandimen Rainfall Station

• Main Authors: Jhih-SianChang, 張志賢
• Other Authors: Pei-Hwa Yen
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/pa43v4

碩士 === 國立成功大學 === 水利及海洋工程學系碩博士班 === 101 === ABSTRACT Pecuniary losses caused by natural disaster reach about 10 billion NT dollars average per year in Taiwan with which 70% damage created by the typhoon attack. Typhoon route as well as typhoon rainfall forecasting accurately and rapidly would be the key to disaster precaution in which relative study of typhoon events would be needed by data analysis, theoretical research, experience evaluation or numerical calculation. So, it pays an important role to develop the typhoon rainfall forecasting model for warning system to reduce risk of damage during the typhoon season and to formulate precaution strategy before disaster occurred. A forecasting model of typhoon rainfall developed by the GMDH (Group Method of Data Handling) structure of Self-Organization Algorithm with four parameters of rainfall (R), maximum wind speed of typhoon center (V), Terrain block (E) and distance (D) between the target location and typhoon center is proposed in this paper. Data of these 4 parameters observed at Sandimen Rainfall Station and from CWB were be used to construct the prior 3hrs and prior 1hr typhoon rainfall forecasting model to provide the necessity of warning facility with data of eleven typhoon events during 2006 to 2011. Then, recursive GMDH model cloud be reorganized by using the update data to match the time variant properties in forecasting steps to improve the predict accuracy. The modeling approach shows that the GMDH algorithm is better than Stepwise regression GMDH (SGMDH) on typhoon rainfall forecasting. Prior 1hr typhoon rainfall forecasting model constructed by taking combined data of 4 typhoon events would forecast typhoon rainfall of other 11 typhoon events of whole duration and concentrate period and show that the average RMSE, CC, MAD and total rainfall deviation are in between 9.64~12.23mm, 54.03~66.70%, 5.68~8.25mm and 14.77~19.29% respectively. In addition, taking six typhoon events for example, recursive procedure then bring up to modified the original prior 1hr typhoon rainfall forecasting model and results show that the procedure amend about 26.75% superior in typhoon rainfall forecasting. So, recursive GMDH could substantially reduce rainfall forecasting errors and promote rainfall predicting accuracy. Due to the practical operation of disaster prevention warning facility, a prior 3hrs typhoon rainfall forecasting model was build up by taking combined data of 11 typhoon events and the predicted results reveal that the average RMSE, CC, MAD and total rainfall deviation are in between 8.38~9.16mm, 60.13~70.15%, 5.91~6.61mm and 16.73~18.39% respectively. In consequence of typhoon rainfall forecasting with data of 11 typhoon events, both prior 3hrs and prior 1hr typhoon rainfall forecasting appear the trend of forecasting results in agreement with measuring materials and result reasonably predicting accuracy in this research with the average RMSE, CC, MAD and total rainfall deviation in between 8.38~12.23mm, 54.03~70.15%, 5.68~8.25mm and 14.77~19.29% respectively. Therefore the data acquisition and transmission system coupled with the GMDH forecasting model provided by this paper could possess the practical usage of on-line typhoon rainfall forecasting at the specific surroundings.