Artificial neural network modelling and flood water level prediction using extended Kalman filter
Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (...
| 出版年: | Proceedings - 2012 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2012 |
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| 第一著者: | |
| フォーマット: | Conference paper |
| 言語: | English |
| 出版事項: |
IEEE Computer Society
2012
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875980418&doi=10.1109%2fICCSCE.2012.6487204&partnerID=40&md5=2589d7478e47a71fada3666be0d2096d |
| 要約: | Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero. © 2012 IEEE. |
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| ISSN: | |
| DOI: | 10.1109/ICCSCE.2012.6487204 |