基于LightGBM的大藤峡水利枢纽入库流量预测研究OA

To address the insufficient accuracy of traditional inflow forecasting methods for the Datengxia Water Control Project and improve the precision of 24-hour inflow prediction,this study constructs time-series features including historical inflow,upstream hydrological conditions and rainfall based on multi-source heterogeneous data from the Datengxia Data Center.A total of 24 independent LightGBM models are established to fit the mapping relationships between the above features and the hourly inflow at each moment in the subsequent 24 hours.The verification results show that the mean relative prediction error of the proposed model is 17.54％,which is significantly better than the 32.59％of the rainfall and hydrological monitoring system,with an accuracy improvement of 15.05 percentage points.The LightGBM model can effectively characterize the nonlinear coupling relationships among multiple influencing factors and delivers higher forecasting accuracy,which can provide technical support for flood control and power generation dispatching of the water control project.