Journal of Hydroinformatics Vol 12 No 3 pp 351–364 © IWA Publishing 2010 doi:10.2166/hydro.2009.085

Improving flood forecasting in Bangladesh using an artificial neural network

A. S. Islam

A river stage neural network model has been developed to study and predict the water level of Dhaka city. A total of five stations located at the border area of Bangladesh on the Ganges, Brahmaputra and Meghna rivers are selected as input nodes and Dhaka on the Buriganga river is the output node for the neural network. This model is trained with river stage data for a period of 1998 to 2004 and validated with data from 2005 to 2007. The river stage of Dhaka has been predicted for up to ten days with very high accuracy. Values of R², root mean square and mean absolute error are found ranging from 0.537 to 0.968, 0.607 m to 0.206 m and 0.475 m to 0.154 m, 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 management and reducing the unnecessary cost of field data collection.

Keywords: artificial neural networks; flood forecasting; hydrology; model; rainfall–runoff

Full article (PDF Format)