Efficacy of hybrid neural networks in statistical downscaling of precipitation of the Bagmati River basin

Abstract

Abstract This study investigates and analyses the present and future senarios of precipitation using statistical downscaling techniques at selected sites of the Bagmati River basin. Statistical downscaling is achieved by feed forward neural network (FFNN) and wavelet neural network (WNN) models. Potential predictors for the model development are selected based on the performances of Pearson product moment correlation and factor analysis. Different training algorithms are compared and the traincgb training algorithm is selected for development of FFNN and WNN models. The visual comparison and the statistical performance indices were calculated between observed and predicted precipitation. From the analysis of results, it is evident that WNN models were well capable of (training: RMSE 1.61–1.67 mm, R 0.94–0.952; testing: RMSE 1.68–1.78 mm, R 0.93–0.95) predicting precipitation followed by FFNN model for all the selected sites. Hence, the projected precipitation (2014–2036) is found by WNN model only with inputs as different GCMs data. The projected precipitation results are analysed for the period 2014–2036 and find that there is a decrease in precipitation with respect to base period data (1981–2013) by 66.62 to 84.21% at Benibad, 4.53 to 21.74% at Dhenge and 6.40 to 22.27% at Kamtaul, respectively.