Long-Term Forecast of Watershed Runoff Based on GWO-BP and Multi-Scale Forecasting Factor Analysis

To address limitations such as short forecast periods, data collection challenges, insufficient understanding of physical mechanisms, and single-scale constraints, forecasting factors and their characteristics were analyzed across astronomical, global, and watershed scales. Forecasting factors were selected based on astronomical observations, ocean current predictions, traditional calendars, and agricultural proverbs, and their characteristics were quantitatively processed. A BP neural network optimized by the Gray Wolf Optimizer (GWO) algorithm (GWO-BP) was constructed, and the dataset derived from sample division of the Fengman Reservoir Basin was used to train the model for secondary fitting. The model successfully fit and predicted the annual inflow of the Fengman Reservoir Basin from 2013 to 2017. Through a comparison with the GWO–Support Vector Machine (GWO-SVM) model, results showed that the GWO-BP model exhibited superior predictive performance. This method integrates multi-scale, easily accessible, and quantifiable forecasting factors, facilitating the extension of long-term runoff forecasting applications within the river basin.