Comparing SANN and SARIMA for forecasting frequency of monthly rainfall in Umuahia

Abstract

This paper adopted the use of artificial neural network (ANN) which is also a nonlinear model in contributing to the debate of comparing different methods of forecasting frequency of rainfall in Umuahia, Abia State of Nigeria. The choice of ANN is informed by articles in literature which have shown that Neural Network models outperform some traditional statistical models in modelling meteorological time series. The data used for the research was obtained from the National Root Crop Research Institute, Umudike, Abia State and it spanned from 2006 to 2016. The forecasting performance of seasonal autoregressive integrated moving average (SARIMA) model and that of seasonal artificial neural network (SANN) were compared with four forecast performance measures:- Forecast Error (FE), Mean Forecast Error (MFE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Results from the study showed that the SARIMA had lower error indicators in forecast performance and thus was adjudged better than SANN in forecasting frequency of rainfall in Umuahia, Abia State. A t-test for significant difference showed that there is no statistical significant difference between both forecast values. Hence, the authors conclude that the two methods can be successfully used as substitutes. Farmers, Construction Companies, Government agencies working in Umuahia are therefore advised to choose SARIMA modelling of monthly rainfall frequency over that of SANN. This study also confirms that ANNs are not utterly better than the traditional statistical models as seen in other works in literature.