Forecasting Short-Term Wind Speed using ARIMA, and Convolution Neural Network Models at International Airports in Saudi Arabia

Abstract

This study aimed to predict the maximum wind speed (MWS) at three international airports of Saudi Arabia, such as King Abdulaziz International Airport (KAIA), King Fahd International Airport (KFIA), and King Khalid International Airport (KKIA). For this end, we utilized advanced machine learning (ML) and Statistical techniques, including Artificial Neural Network (ANN), Convolution Neural Network (CNN), and Autoregressive Integrated Moving Average (ARIMA) models, and the daily maximum wind speed data during 2021-2023. To develop the models, we effectively employed the average mutual information (AMI) for assessing the suitable input variables to predict the maximum wind speed in KKIA, KFIA, and KAIA. The MWS forecasting models of three airports were constructed using the training subset (80%, spanning from 2021 to 2023 May) and testing subset (20%, between 2023 May and 2023 Dec). The results showed that the accuracy of the findings has been improved by implementing the proposed CNN models. Compared to the ANN, and ARIMA models, the CNN algorithm showed its remarkable potential as a high-level model in accurately estimating KKIA, KFIA, and KAIA max wind speed values and exhibiting superior generalization capability and minimal variance.