Developing an innovative machine learning model for rainfall prediction in a semi-arid region

Abstract

ABSTRACT Due to global climate change, managing water resources is one of the most critical challenges for most countries in the world, especially in the Middle East. In the Kurdistan Region of Iraq (KRI), there is a good amount of precipitation, surface water, and groundwater, but the main issue is mismanagement of those sources. Rainfall is one of the major sources of water resources in KRI. In order to manage the available water resources and prevent natural disasters such as floods and droughts, there is a need for reliable models for forecasting rainfall. The current study focuses on developing a hybrid model, namely seasonal autoregressive integrated moving average combined with an artificial neural network (SARIMA-ANN) for forecasting monthly rainfall at Sulaymaniyah City for the duration of 1938–2012. For comparison purposes, a conventional machine learning model, namely artificial neural networks (ANN) has been applied on the same data. Two different statistical measurements, namely, root mean square error (RMSE) and coefficient of determination (R2), have been used to check the accuracy of the proposed models. According to the findings, SARIMA-ANN outperformed ANN with RMSE = 11.5, RMSE = 51.002, R2 = 0.98, R2 = 0.43, respectively. The findings of the current study could contribute to Sustainable Development Goal (SDG) 6.