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.

本研究使用统计降尺度技术，在巴格马蒂河流域的选定地点，对当前和未来的降水量进行了调查和分析。统计缩减是通过前馈神经网络（FFNN）和小波神经网络（WNN）模型实现的。基于Pearson产品矩相关性和因子分析的性能，选择模型开发的潜在预测变量。比较了不同的训练算法，并选择了traincgb训练算法来开发FFNN和WNN模型。在观测到的和预测的降水之间计算了视觉比较和统计性能指标。从结果分析来看，很明显WNN模型具有很好的能力（训练：RMSE 1.61-1.67 mm，R 0.94-0.952；测试：RMSE 1.68-1.78 mm，R 0.93-0。95）预测降雨，然后选择所有站点的FFNN模型。因此，WNN模型仅在输入为不同GCM数据的情况下才能找到预计的降水量（2014-2036）。分析了2014-2036年期间的预计降水结果，发现与基准期数据（1981-2013年）相比，贝尼巴德的降水减少了66.62％至84.21％，Dhenge的降水减少了4.53至21.74％，而6.40至22.27 ％分别在Kamtaul。