1993 Fiscal Year Final Research Report Summary
PREDICTION FOR HEAVY RAINFALL USING NEURAL NETWORKS
| Project/Area Number |
04805049
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Hydraulic engineering
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
MORIYAMA Toshiyuki KYUSHU UNIV.DETP.OF ENG.RESEARCH ASSOCIATE, 工学部, 助手 (50136537)
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| Co-Investigator(Kenkyū-buntansha) |
TANIGUCHI Rinichiro KYUSHU UNIV.INTERDISCIPLINARY GRADUATE SCHOOL OF ENG.SCIENCE ASSOCIATE PROFFESOR, 総合理工学研究科, 助教授 (20136550)
HITANO Muneo KYUSHU UNIV.DETP.OF ENG.PROFFESOR, 工学部, 教授 (50037850)
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| Project Period (FY) |
1992 – 1993
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| Keywords | Heavy rainfall disaster / Debris flow / Sediment disaster / Rrainfall prediction / Neural networks / 降水レーダ |
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| Research Abstract |
The goal herein is to establish a prediction method for a degree of risk by certain rainfalls , as a countermeasure against sediment disaster which caused by rain. Thus, the time of concentration and critical rainfall are defined, A prediction system using neural network is constructed. It is expected that the neural network can learn a general rule from examples.
The main procedure of the method are as follows ; cumulative rainfalls on various times are calculated from a rainfall time series. The rainfalls pattern which is normalized and teach signal of occurrence 0.99 or non-occurrence 0.01 of disaster are given to the system. After the neural network is optimized by back-propagation learning, the system automatically calculates the degree of risk from 0 to 1 for new rainfalls pattern.
The prediction system is applied to three types of real cases and the results are as follows ; It is applicable for predicting the occurrence of debris flows at Unzen volcano, because the system has high accuracy in judgment. On secondary disaster after typhoon No.9119 at the Chikugo river basin, change in critical rainfall after the typhoon is recognized. Moreover the time of concentration and critical rainfall are found. It is clarified that the antecedent rainfall affects the occurrence of sediment disasters in Kagoshima city.
This system is good tool for prediction of sediment disaster and to estimate the occurrence criteria. Therefore, the prediction system using the neural network for the prediction of the rainfall rate seems to be developing more in the future.
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