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 (...
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Adnan R.; Ruslan F.A.; Samad A.M.; Zain Z.M. |
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Adnan R.; Ruslan F.A.; Samad A.M.; Zain Z.M. 2-s2.0-84875980418 Artificial neural network modelling and flood water level prediction using extended Kalman filter 2012 Proceedings - 2012 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2012 10.1109/ICCSCE.2012.6487204 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875980418&doi=10.1109%2fICCSCE.2012.6487204&partnerID=40&md5=2589d7478e47a71fada3666be0d2096d 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. IEEE Computer Society English Conference paper |
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2-s2.0-84875980418 |
| spellingShingle |
2-s2.0-84875980418 Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| author_facet |
2-s2.0-84875980418 |
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2-s2.0-84875980418 |
| title |
Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| title_short |
Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| title_full |
Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| title_fullStr |
Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| title_full_unstemmed |
Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| title_sort |
Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| publishDate |
2012 |
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Proceedings - 2012 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2012 |
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| doi_str_mv |
10.1109/ICCSCE.2012.6487204 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875980418&doi=10.1109%2fICCSCE.2012.6487204&partnerID=40&md5=2589d7478e47a71fada3666be0d2096d |
| description |
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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IEEE Computer Society |
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English |
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Conference paper |
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scopus |
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Scopus |
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1828987884055560192 |