River Stage Forecasting in Bangladesh: Neural Network Approach

Abstract

A relatively new approach, artificial neural network, was demonstrated in this study to be a highly suitable flow prediction tool yielding a very high degree of water-level prediction accuracy at Dhaka, Bangladesh, even for up to 7 lead days. The goodness-of-fit R² value, root-mean-square error, and mean absolute error, ranging from 0.9164 to 0.9958, 0.0788 to 0.2756 m, and 0.0570 to 0.2050 m, respectively, were obtained from the training and verification simulation. In addition, the high degree of accuracy is accompanied with very small computational time. Both results make the artificial neural network a desirable advanced warning forecasting tool. Sensitivity analysis was also performed to investigate the importance of each of the input neurons. The sensitivity study suggested a reduction of three from eight initially chosen input neurons. The reduction has insignificantly affected the prediction accuracy level. The finding enables the policymakers to reduce the unnecessary data collection at some gauging stations and, thus, results in lower costs.