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http://dx.doi.org/10.3745/KIPSTB.2011.18B.5.271

Performance Comparison between Neural Network Model and Statistical Model for Prediction of Damage Cost from Storm and Flood  

Choi, Seon-Hwa (소방방재청 국립방재연구소)
Publication Information
The KIPS Transactions:PartB / v.18B, no.5, 2011 , pp. 271-278 More about this Journal
Abstract
Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.
Keywords
Damage from Storm and Flood; Prediction of Damage; Neural Network; Pattern Recognition; Model Optimization; Kernel Density Function;
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Times Cited By KSCI : 5  (Citation Analysis)
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