Integration of VGGNet-Driven Precipitation Similarity Analysis and Hydrological Modeling for Enhanced Streamflow Forecasting

Accurate medium- and long-term hydrological forecasting is crucial for sustainable water management, infrastructure planning, and ecosystem conservation. This study integrates visual geometry group convolution neural network algorithm (VGGNet)-driven precipitation similarity analysis with hydrological modeling to enhance streamflow forecasting accuracy. Traditional process-driven hydrological models, which rely on detailed physical mechanisms, often falter in data-scarce regions, whereas data-driven models, despite their simplicity, lack interpretability. By combining these approaches, we propose a novel methodology that leverages the image recognition capabilities of convolutional neural network, specifically VGGNet, to perform precipitation similarity analysis. This method transforms monthly precipitation data into daily forecasts, addressing the challenge of temporal downscaling. The Danjiangkou Basin in China serves as the study area, with historical precipitation and streamflow data from 1982 to 2015. The VGGNet model identifies historical months with similar precipitation patterns, and the Soil and Water Assessment Tool model is used to predict streamflow. Results demonstrate significant improvements in forecasting accuracy, with Pearson Correlation Coefficient of 0.89 and Nash-Sutcliffe Efficiency of 0.79. This integrated approach shows promise for advancing medium- and long-term hydrological forecasting techniques, providing a robust tool for effective water resource management.