Monthly Precipitation Forecast in Shaoyang Region Based on Sunspots

The prediction of monthly precipitation is of great significance for water resource utilization and water conservancy project scheduling. The core work of this study includes: proposing a new sunspot R_SSNy index and constructing an RBF forecasting model; construct a Multilayer-Perceptron (MLP) monthly precipitation forecast model based on the R_SSNy index prediction value; construct the Rainfall Abundance Index (RAI) to overcome the contradiction between local and global optima, and using MLP for RAI prediction. The results show that: the Radial Basis Function network (RBF) forecasting model of R_SSNy index performs well in the calibration, validation, and prediction stages, with a relative error of less than 10% for 93% of the predicted values in month t+1; the training and testing sets of the MLP monthly precipitation forecast model have good performance, with R² values of 0.99 for both sets; the defined RAI index provides an effective tool for the quantitative assessment of precipitation abundance and scarcity states, and holds value for further research deepening and application promotion; the MLP monthly RAI forecast model performs well and meets the accuracy requirements. The research on monthly precipitation forecast based on sunspots has pioneering significance.