Rainfall Forecasting in Narmada Basin of Gujarat (India) by Artificial Neural Network Technique

Rainfall is a very important parameter in hydrological model. Many techniques and models have been developed for rainfall forecasting. In this study an artificial neural network (ANN) based model was developed for rainfall forecasting. Proposed model used Multilayer perceptron (MLP) network with feed forward back propagation algorithm for training. Several models have been developed in hydrological forecasting yet; the Artificial Neural Network (ANN) model provides a quick and flexible means of creating reasonable output. It has also been shown a higher performance level when compared with conventional methods. This study present a method of rainfall forecasting by developing anANN-based model using major weather variables such as dry bulb temperature, wet bulb temperature, relative humidity, pan evaporation, vapour pressure as inputs while the rainfall as the target output. As part of the ANN model development procedures, the data sets of 11956 data in the study area was partitioned into two parts with 70% of the entire data sets used as the training data while the remaining 30% used as the testing and the validation data. The proposed model has been able to predict values with suitable results. The prediction is made on the bases of previousdata. For the evaluation of the results and the ability of the developed prognostic models, appropriate statistical indexes such as the coefficient of determination (R²), the Root mean square error (RMSE), Mean square error (MSE), Nash-Sutcliffe efficiency (EF), Akaike information criteria (AIC), Bayesian information criteria (BIC) were used. The findings from this analysis showed that the ANN model 5-5-3-1 provides satisfactory results based on statistical indexes.