A Study of Flood Inundation Extent EstimationUsing Aritificial Neural Networks

• Main Authors: ZI-HONG ZHUANG, 莊子弘
• Other Authors: Chiu-Chang Li
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/05066450864503746408

碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 98 === At present, flood defense strategies of the flood authorities are to estimate the flood inundation extent and maximum flood depth from the existing flood inundation potential database generated by the two-dimensional overland flow simulation model with several pre-designed rainfall patterns and scenarios. However, it cannot provide an effectively and timely flood inundation forecast due to the real-time storm rainfall. This study presents hybrid models to build the regional flood inundation forecasting model. The core part is Back-Propagation Neural Network (BPNN) that is used to forecast flood depth and to estimate the area of flood inundation. There are three parts for building the proposed hybrid models: (1) building one to three-hour ahead flood depth forecasting models of security spots due to the real-time storm rainfall, (2) building flooding area estimation models to estimate the flooding area of the security region, (3) creating the lookup tables used to transform a specific elevation into the corresponding cumulative area; then, the flood inundation map can be shown. In this study, the results show that BPNN can be successfully applied with high accuracy for one to three-hour ahead flood depth forecasting. The correct percentages of forecasting flood depths are 91%, 91% and 90% when the threshold of flood depth is 10 cm; 86%, 87% and 86% when the threshold of flood depth is 30 cm. For estimating flooding area, the proposed approach also performs well; the correct percentages are very high for the pre-designed rainfall patterns and the larger storms.