ABSTRACT
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 root
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.
Keywords: - Correlation, Mean Square Error, Model Efficiency, Neural Network, Rainfall