Prediction of Rainfall Using MLP and RBF Networks

Prediction of rainfall for a region is of utmost importance for planning, design and management of irrigation and drainage systems. This can be achieved by different approaches such as deterministic, conceptual, stochastic and Artificial Neural Network (ANN). This paper illustrates the use of ANN for prediction of rainfall at Atner, Multai and Dharni stations. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks are applied to train the network data. Model performance indicators such as correlation coefficient, model efficiency and ischolar_main mean square error are used to evaluate the performance of the MLP and RBF networks. The paper presents the MLP network is better suited for prediction of rainfall for Atner and Multai whereas RBF network for Dharni.