水力发电2024，Vol.50Issue(12)：18-24,32,8.
基于随机森林的恩施地区降水预报订正方法研究
Revision Method of Precipitation Forecast in Enshi Area Based on Random Forest
摘要
Abstract
Numerical forecasting is the main method for short-and medium-term precipitation forecast,and its output is often biased,so it is necessary to revise the numerical forecast.The wide spread of hydropower stations in Enshi area puts higher requirements for rainfall forecast accuracy,and the existing revision methods are region-specific and thus difficult to be directly applied to Enshi area.For this reason,this paper takes the Banli Yuan Rainfall Station in Enshi area as an example,and uses the random forest method to establish the nonlinear regression relationship between various forecast factor output from the numerical forecasting model and the actual precipitation,to form the rainfall revision model of the numerical forecasting products and analyze the time scale effect,which will provide the theoretical basis for the selection of the high-precision revision model for the Enshi area.Seven years of historical data from 2016-2022 are applied to construct the revised model using the random forest method,and compared with other machine learning such as support vector machine and long and short-term memory network.The experimental results show that the machine learning method can significantly improve the accuracy and reliability of precipitation forecasting compared with numerical forecasting for rainfall prediction,and among the three machine learning methods,the random forest has the best effect.关键词
机器学习/降水预报/预报因子/偏差订正/随机森林Key words
machine learning/precipitation forecasting/forecast factor/bias revision/random forest分类
建筑与水利引用本文复制引用
卢松,高光平,张毅,纪洪岑,姚志武,尹章才..基于随机森林的恩施地区降水预报订正方法研究[J].水力发电,2024,50(12):18-24,32,8.基金项目
国家自然科学基金资助项目(42171415) （42171415）
