2003 Fiscal Year Final Research Report Summary
Study on Real-Time Forecasting of Hydrological Variables Using Technique of Non-Linear Time Series Analysis
| Project/Area Number |
14560199
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Irrigation, drainage and rural engineering/Rural planning
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| Research Institution | Okayama University |
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Principal Investigator |
CHIKAMORI Hidetaka Okayama University, Faculty of Environmental Science and Technology, Associate Professor, 環境理工学部, 助教授 (40217229)
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| Co-Investigator(Kenkyū-buntansha) |
NAGAI Akihiro Okayama University, Faculty of Environmental Science and Technology, Professor, 環境理工学部, 教授 (80093285)
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| Project Period (FY) |
2002 – 2003
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| Keywords | flood forecasting / rainfall forecasting / non-linear time series analysis / chaos |
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| Research Abstract |
For effective flood control, accurate real-time forecasting of flood discharge is very important. For supporting the flood forecasting, accurate real-time forecasting of rainfall is important as well. In this study, we developed new flood and heavy rainfall forecasting system using technique of non-linear time-series analysis, that is, local linear approximation (LL) method, Nearest Neighbor (NN) method and Self-Organizing feature map with Linear Output mapping (SOLO) algorithm, and examined forecasting accuracy of the developed system. First, we applied LL method to real-time flood forecasting for 39 storms records observed at Kuroki Dam Basin located in the northern part of Okayama Prefecture, Japan, during 1979 -2002. It is, as a result, found that forecasting accuracy of flood discharge by the LL method is so high that it is comparative to that by the conventional real-time forecasting system using the Tank Model with Kalman filtering technique. Second, we also applied the SOLO algorithm to real-time flood forecasting at Kuroki Dam Basing and found that the flood forecasting system by the SOLO algorithm achieved a high degree of accuracy in the case of using feature vectors composed of scores of all principal components of past discharge and rainfall data. The accuracy is almost equivalent to that by the LL method. However, when feature vectors are directly composed of discharge and rainfall data, forecasting accuracy became worse particularly during verification duration. Finally we applied the NN method to real-time rainfall forecasting at Okayama using the rainfall data observed at ground rain gauges around Okayama of Automated Meteorological Data Acquisition System (AMeDAS) of Japan. Although forecasting accuracy of rainfall occurrence was so high as over 80%, accuracy of forecasted rainfall depth was insufficient for practical use because it tends to be underestimated.
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Research Products
(2 results)
