Modelling of Daily Rainfall - Runoff Using Multi-Layer Perceptron Based Artificial Neural Network and Multi-Linear Regression Techniques in A Himalayan Watershed - Chanu, Sanjarambam Nirupama; Kumar, Pravendra (Indian Journal of Hill Farming, 2018)
Modelling of rainfall-runoff is considered one of the prerequisite of hydrological processes forvarious applications involving conservation and management of water resources. In this study, two techniques that is Multi-Layer Perceptron (MLP) neural network, which is well known efficient Artificial Neural Network (ANN), and Multi-Linear Regression (MLR) were applied for modelling daily rainfall-runoff and results obtained were compared. In order to simulate the processes, time series monsoon data of ten years (2000-2009) of rainfall and runoff at Bino watershed in Almora and Pauri Garhwal districts of Uttarakhand, India were used. In addition, Gamma Test (GT) was used for identifying the best input combinations for rainfallrunoff modelling. Performance of models was evaluated qualitatively as well as quantitatively employing statistical indices viz. correlation coefficient (r), root mean square error (RMSE) and coefficient of efficiency (CE), both for training as well as testing. Different MLP based ANN models were developed with the change of number of neurons and hidden layers and best model among them was selected based on performance indices. The same inputs were used to develop MLR model. The r, RMSE and CE values of best performing MLP model were found to be 0.95, 1.27 (mm) and 0.88, respectively during training while their corresponding values during testing were determined to be 0.92, 0.96 (mm) and 0.80. The comparison of both MLP and MLR models reveals that MLP based ANN is superior in performance for rainfall-runoff modelling and able to predict the daily runoff with good accuracy for the study area.