A hybrid spatio-temporal modelling: an application to space-time rainfall forecasting

Abstract

Efficient and reliable forecasting techniques for various climatic conditions are indispensable in agricultural dependent country like India. In this context, rainfall forecasting is one of the most challenging tasks because of the existence of three patterns, viz., temporal, spatial, and non-linear, simultaneously. Space-Time Autoregressive Moving Average (STARMA) model is one of the promising and popular approaches for modelling spatio-temporal time series data. However, the observed features of many space-time rainfall data comprise complex non-linear dynamics and modelling these patterns often go beyond the capability of conventional STARMA model. Moreover, despite the popularity of artificial neural network (ANN) and support vector machine (SVM) for modelling complex non-linear dynamics, they are not capable to deal with spatial patterns. To overcome the problem, a new spatio-temporal hybrid modelling approach has been proposed by integrating STARMA, ANN, and SVM as well. The proposed approach has been empirically illustrated on annual precipitation data of six districts of northern part of West Bengal, India. The study reveals that proposed spatio-temporal hybrid approach has better modelling and forecasting precision over conventional STARMA as well as most widely used Autoregressive Integrated Moving Average (ARIMA) model.