Research on Waterway Traffic Volume Prediction Based on Artificial Intelligence

Accurate prediction of waterway transportation volume is crucial for ensuring the safety of water transportation. In this study, a comprehensive analysis and preprocessing of regional waterway transportation data from the past decade were conducted by combining statistical analysis and deep learning techniques. A time series forecasting model based on LSTM (Long Short-Term Memory) was created and further improved in performance through ensemble learning. Experimental results demonstrate that compared to traditional single models, this ensemble model shows significant improvements in accuracy, effectively reducing prediction errors, and confirming the effectiveness of deep learning in the field of waterway transportation volume prediction. This achievement not only provides a scientific decision support tool for government and relevant departments to ensure the safety and efficiency of water transportation but also promotes technological advancement and innovation in this field, showcasing the tremendous potential of deep learning techniques in complex real-world applications.