气象科学2024，Vol.44Issue(6)：1154-1162,9.DOI:10.12306/2021jms.0098
机器学习算法对黄河上游人工增雨效果检验的应用
Application of machine learning algorithm in the effect verification of artificial rainfall on the upper reaches of the Yellow River
摘要
Abstract
Based on the precipitation data from 8 national stations in the upper Yellow River from 1976 to 2020 combined with 74 circulation indexes,the random forest algorithm and the BP neural network algorithm were used to establish the monthly precipitation grade and daily precipitation grade prediction models,and then the prediction effect of the two models on actual precipitation as well as the stability of the model were tested.The influence of precipitation was then tested by combining the actual precipitation and the predicted precipitation,and finally an appropriate prediction model was chosen to estimate natural precipitation.Results show that the two algorithms perform better in simulating monthly and daily precipitation,with model accuracy reaching 80％when samples account for less than 75％of the total samples.However,the results for the prediction of extreme monthly precipitation are subpar.The daily precipitation model created by the BP neural network has an error in the prediction period that is higher than that in the training period,and the model's stability is relatively poor.In contrast,the model created by the RF algorithm was more stable and has good tolerance for nonlinear effects and omissions.In Gande County,from 2019 to 2020,the impact of ten rocket operations to increase rainfall was examined.The two algorithms both projected level 1 precipitation for September 18,2020,however level 2 precipitation actually fell on that day.The effectiveness of the artificial rainfall enhancement operation is thought to be good.关键词
机器学习/环流指数/降水/随机森林/神经网络Key words
machine learning/circulation index/precipitation/random forest/the neural network分类
天文与地球科学引用本文复制引用
张玉欣,康晓燕,侯永慧,薛丽梅,田建兵..机器学习算法对黄河上游人工增雨效果检验的应用[J].气象科学,2024,44(6):1154-1162,9.基金项目
第二次青藏高原综合科学考察研究项目(2019QZKK0104) （2019QZKK0104）
国家自然科学基金资助项目(42165008) （42165008）
西北区域人影建设研究项目(RYSY201903) （RYSY201903）
青海省自然科学基金资助项目(2021-ZJ-745) （2021-ZJ-745）
