Forecasting runoff coefficients using ANN for water resources management: The case of Notwane catchment in Eastern Botswana

Forecasting future response behaviour of a semi-arid catchment in terms of runoff coefficient being trivial, an attempt has been made to apply an artificial neural network (ANN) model to forecast the runoff coefficients (ROC) for the rapidly urbanizing Notwane catchment system in Botswana. Runoff coefficients computed from 1978 to 2000, by the water balance technique have been used to develop the optimal network architecture with appropriate choice of the size of input vectors, number of hidden layers and number of neurons in the hidden layers, training algorithms and transfer functions for the network. The developed network has then been used to simulate the runoff coefficients for the above period. Based on its performance in terms of reproducibility of the water balance runoff coefficients, the network was used to forecast the runoff coefficients up to 2020 and it was found that the increase in runoff coefficients is about 1% per year. Based on the weights given by the network to the input variables, it was found that while about 48% contribution came from climatic factors, 52% came from the land use/land cover.