Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition

Key Points

• Improved streamflow prediction accuracy demonstrates a 20% reduction in prediction error over traditional methods.
• Advanced deep learning models utilized outperformed baseline models, highlighting their effectiveness in handling complex datasets.
• Application of seasonal-trend decomposition techniques revealed insights into streamflow patterns over multiple seasons, enhancing forecasting accuracy.
• These findings indicate the need for integrating machine learning approaches in hydrological modeling for better resource management.