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<p>Weather forecasting is a critical task in daily life, as it helps people and organizations prepare for supply chain operations impacted by climate change, especially in Saudi Arabia, where changes in wind speed and high temperatures are significant factors. This forecasting aims to mitigate potential damage to vehicles and the products being transported. The logistics of routing the progress of goods through the supply chains rely significantly on weather forecasts. One of the threats posed by climate change to supply chains is the potential damage to vehicles and goods moving from one place to another. Therefore, this paper introduces a new weather prediction system that will help supply chains in Saudi Arabia choose the optimal time and place to conduct operations. The feature selection and a two-stage predictive model are the two main components of the proposed system. The Fox metaheuristics algorithm is applied in the feature selection as an indicator for the system while a bidirectional gated recurrent unit is performed in the two-stage predictive model. During the two-stage predictive model, the selected features as wind speed and temperature are applied in the first and second stages to obtain the outcome. The proposed system achieves an acceptable accuracy, with a mean absolute error of 5.6799 in predicting air temperature and 1.4714 in predicting wind speed, indicating good performance in predicting the numeric values of wind speed and air temperature.</p>

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Two-Stages Weather Forecasting System for Optimizing Supply Chain Operations in Saudi Arabia Using BiGRU and Fox Metaheuristics Algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • Omar Alghushairy,
  • Raed Alsini,
  • Ayman Yafoz,
  • Saud Yonbawi,
  • Xiaogang Ma

摘要

Weather forecasting is a critical task in daily life, as it helps people and organizations prepare for supply chain operations impacted by climate change, especially in Saudi Arabia, where changes in wind speed and high temperatures are significant factors. This forecasting aims to mitigate potential damage to vehicles and the products being transported. The logistics of routing the progress of goods through the supply chains rely significantly on weather forecasts. One of the threats posed by climate change to supply chains is the potential damage to vehicles and goods moving from one place to another. Therefore, this paper introduces a new weather prediction system that will help supply chains in Saudi Arabia choose the optimal time and place to conduct operations. The feature selection and a two-stage predictive model are the two main components of the proposed system. The Fox metaheuristics algorithm is applied in the feature selection as an indicator for the system while a bidirectional gated recurrent unit is performed in the two-stage predictive model. During the two-stage predictive model, the selected features as wind speed and temperature are applied in the first and second stages to obtain the outcome. The proposed system achieves an acceptable accuracy, with a mean absolute error of 5.6799 in predicting air temperature and 1.4714 in predicting wind speed, indicating good performance in predicting the numeric values of wind speed and air temperature.