Enhancing Weather Prediction for Transportation and Logistics Using the Temporal Fusion Transformer Algorithm

Towards optimization of transportation and logistics operations, recent developments in state-of-the-art methodologies examined weather prediction utilizing a variety of machine learning and deep learning techniques. Accurate weather forecasting is essential for supply chain resilience, effective route planning, and the mitigation of potential risks. This study evaluates the effectiveness of three weather prediction models created especially to meet the demands of the logistics and transportation industry: TFT, SVM, and RL. The accuracy, computational efficiency, and practicality of the models were assessed using a large dataset of historical weather patterns. The findings show that the TFT model works better than SVM and RL in capturing temporal relationships in the dataset, with a higher accuracy where temporal data's long-term dependencies are captured. This study highlights the potential of the TFT model to provide robust and reliable weather forecasts, making it a preferred choice for applications in transportation and logistics.