Modeling and Forecasting of Rainfall Time Series. A Case Study for Pakistan Tayyab Raza Fraz

Abstract

The change of weather conditions is considered as the major problem, particularly for developing country like Pakistan. Machine learning and artificial neural network models have become attractive forecast techniques for rainfall as compared to traditional statistical methods in the last few years. The behavioral pattern in rainfall (mm) annually by 1901 to 2020 is studied. Moreover, forecasts of three models based on past observations are evaluated. Fundamentally, different techniques are used for model development. Three modeling techniques include a traditional linear time series ARMA model, an emerging nonlinear threshold technique SETAR model, and influential machine learning technique NAR model. Evaluation of forecast performance is based on three forecast error criteria namely MSE, RMSE, and MAPE. Results indicate that the rainfall (mm) will slightly increase in the coming ten years i.e. 2021 to 2030. Furthermore, the findings also reveal that the NAR model is a suitable and appropriate model to forecast the rainfall which outperforms the ARMA as well as the SETAR model.