STOCHASTIC MODELLING BASED MONTHLY RAINFALL PREDICTION USING SEASONAL ARTIFICIAL NEURAL NETWORKS

Abstract

India is an agrarian society where 13.7% of GDP and 50% of workforce are involved with agriculture. Rainfall plays a vital role in irrigating the land and replenishing the rivers and underground water sources. Therefore the study of rainfall is vital to the economic development and wellbeing of the nation. Accurate prediction of rainfall leads to better agricultural planning, flood prevention and control. The seasonal artificial neural networks can predict monthly rainfall by exploiting the cyclical nature of the weather system. It is dependent on historical time series data and therefore independent of changes in the fundamental models of climate known collectively as manmade climate change. This paper presents the seasonal artificial neural networks applied on the prediction of monthly rainfall. The amounts of rainfall in the twelve months of a year are fed to the neural networks to predict the next twelve months. The gradient descent method is used for training the neural networks. Four performance measures viz. MSE, RMSE, MAD and MAPE are used to assess the system. Experimental results indicate that monthly rainfall patterns can be predicted accurately by seasonal neural networks.