Improving flood forecasting in Bangladesh using an artificial neural network | Zendy

A. K. M. Saiful Islam | Zendy

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Improving flood forecasting in Bangladesh using an artificial neural network

Author(s) -

A. K. M. Saiful Islam

Publication year - 2009

Publication title -

journal of hydroinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.654

H-Index - 50

eISSN - 1465-1734

pISSN - 1464-7141

DOI - 10.2166/hydro.2009.085

Subject(s) - artificial neural network , stage (stratigraphy) , flood myth , mean squared error , mean absolute error , hydrology (agriculture) , environmental science , water level , meteorology , water resource management , statistics , geography , engineering , computer science , geology , cartography , mathematics , machine learning , geotechnical engineering , paleontology , archaeology

predicted for up to ten days with very high accuracy. Values of R 2 , root mean square and mean absolute error are found ranging from 0.537 to 0.968, 0.607m to 0.206m and 0.475m to 0.154m, respectively, during training and validation of the model. The results of this study can be useful for real-time flood forecasting by reducing computational time, improving water resources

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