Abstract:
The present study is focused on simulating the impact of climate change on the behavior of
precipitationof Kshipra river basin in Madhya Pradesh, India.Artificial neural network (ANN) model was
used to construct of the downscale precipitation scenario. A General Circulation Model (GCM) viz. Hadley
Centre Coupled Model, version 3 (HadCM3), from Hadley Center, UK has been used for the study. In
Model, monthly weather data on the basis of rapid economic growth under A1B scenario (A balanced
emphasis on all energy sources) were considered. The four predictor variables which are used in ANN
model formulation are screened from a set of 26 predictors based on correlation analysis of observed
precipitation. The basic ANN architecture was optimized for training of the model byfirst selecting the
training algorithm and then varying the number of neurons in the hidden layer. Twelve different training
algorithms have been used. Further, the model was evaluated by varying the number of neurons from 1
to 30 in the hidden layer.The performance of modelwas evaluated in terms of the correlation coefficient
(R), mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The
results of model revealed that the predicted precipitation and observed precipitation are better correlated
(R=0.911 and 0.853 during training and validation runs) with back propagation variable learning rate
“traingdx” algorithm.
