An Intelligent Ensemble Learning Framework for Visibility and CAVOK Prediction to Support Logistics Operations

Le résumé fourni par la source

The supply chain operation is fully dependent on weather conditions due to the damage that produce may face during bad weather or accidents that may occur because of cloud cover, especially in Saudi Arabia, which experiences frequent cloud cover throughout the year. In this study, a new weather prediction model based on machine learning and optimization algorithms is proposed to forecast visibility distance and Cloud and Visibility OK (CAVOK), a term indicating good weather, while also estimating cloud cover, which affects flight operations. The proposed system follows two main steps: feature selection and prediction, to predict CAVOK and visibility distance. In the feature selection phase, Fick’s Law Algorithm (FLA) is used to choose the optimal parameters that can serve as indicators in the prediction phase, enhancing the proposed system's performance. In the prediction phase, a new two-stage ensemble algorithm is used to predict sky CAVOK and visibility distance. In the first stage, a new voting ensemble algorithm consisting of Bagging Regressor and Extra Trees Regressor algorithms is proposed to predict the visibility distance. The predicted visibility distance value is then used alone to predict sky CAVOK using the CatBoost Classifier in the second stage. The experimental results show that the proposed system demonstrates reasonable predictive performance, obtaining 529.7506 m, 1174.6118, and 0.3233 for Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R², respectively, in predicting visibility distance. Additionally, the system achieved competitive results while maintaining robust predictive capability in predicting sky CAVOK.

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