Water level prediction by artificial neural network in a flashy transboundary river of Bangladesh

This paper presents the sensitivity analysis results of feed forward multilayer perceptron based Artificial Neural Network model for water level prediction in a data constraint transnational Surma River of Bangladesh. Catchment characteristics, hydro-geomorphological, meteorological and headwater information of the upper catchment area are not available to the authors. As such past daily total rainfall and water levels data available within the country are utilized in this study. Logistic sigmoid activation function with unit steepness parameter is exercised for non-linear transformations in both hidden and output layers. Synaptic weights are adjusted using modified delta rule through error back propagation algorithm. Batch mode of training is adopted for global error minimization. Finally, statistical indicators are used to evaluate the prediction performance of the neural network. The model is then applied to predict water levels with twenty four and forty eight hours lead time.