Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks

Abstract

In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) are studied to forecast the amount of rainfall in Mauritius. Indeed due to the geographical location of Mauritius, the rainfall pattern is deeply affected by the season prevailing whereby the period of summer receives a relatively high amount of rainfall when compared to winter. As such, forecasting rainfall can help the local authorities to manage the distribution of water in the country especially during droughts. The results obtained from both methods are compared in terms of their mean square error, mean absolute difference and mean absolute percentage difference. It is then seen that artificial neural network is a much better model as it is more accurate. This is due to its nonlinearity characteristic and ability to learn and train itself.

Short-term daily precipitation forecasting with seasonally-integrated autoencoder

“…For these reasons, some attention has turned to more complex models for time series forecasting, such as support vector regression [23,24,25,26,27,28], random forest regression [29,30,31,32,33], and artificial neural network. There is a series of works that studied neural networks and gave experimental results showing that they generally outperform linear models in very short-and short-term forecasts [34,35,36,37,16,38,39].…”