A River Flood Forecast Model with Data Assimilation Based on Ensemble Kalman Filter
碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 99 === Taiwan is located on subtropical area of the west Pacific Ocean. The weather patterns have affected by the monsoon and typhoons. The averaged annual rainfall is about 2500 millimeter which is about 2.6 times of the averaged precipitation over the world. The...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/44046627192830908544 |
| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 99 === Taiwan is located on subtropical area of the west Pacific Ocean. The weather patterns have affected by the monsoon and typhoons. The averaged annual rainfall is about 2500 millimeter which is about 2.6 times of the averaged precipitation over the world. The terrain’s steep slope of mountainous areas and heavy rainfall usually cause flooding disaster to make enormous losses in downstream plain where high-density population located. Taipei city where is situated at the Tanshui river basin is the largest city in Taiwan. A flood forecasting model for Tanshui river has been developed in this study to offer a precise flood stage forecast in advance for flood-damaged mitigation.
The flood forecasting model integrated the dynamic routing methods with initial value correction and the artificial neural network(ANN) techniques. The statistical quantities are obtained by the ANN results of predicted water stages with 1-3 hours lead time for several typhoons in the past. The Kalman filter is employed to correct ANN prediction values. The stages predicted with 1-3 hours lead time by Kalman filter are taken as the target values applying in flood forecasting model. Then the ensemble Kalman filter (EnKF) river flood forecasting model is developed to provide accurate and detailed flood information for the Tanshui basin at typhoon period. The flood forecasts can be used for flood alert, evacuation and emergency response.
The study uses the Kalman filter to correct ANN prediction value and the ensemble Kalman filter for data assimilation. The simulated results show that the present model can be effectively to improve the accuracy of flood forecasting and reduce the error propagation with the forecasting lead time. The study can provide more accurate and reasonable flood stages during typhoons period.
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