摘要
中长期径流预报方案是水电站中长期调度计划制订的基础。以长江上游流域宜昌站为研究对象,在1981~2019年宜昌站以及相关区间降雨径流数据的基础上,引入130个遥相关气候因子,基于偏互信息筛选输入因子,应用LSTM神经网络模型建模,对宜昌站的月径流预报方案进行比选。结果表明:长江上游来水与半年前的西太平洋副高和厄尔尼诺指数存在较强的相关性;在遥相关气候因子之外,宜昌站径流与去年同期宜昌站径流、上一月的岷沱江、乌江和雅砻江降雨关系密切。若能引入预报当月的嘉陵江、宜宾-重庆段降雨,可有效提升宜昌站的中长期径流预报精度。
Medium and long-term runoff forecasting scheme is the basis for the medium and long-term dispatch plans of hydropower stations.Taking Yichang Station in the upper reaches of the Changjiang River as the main research object, based on the rainfall and runoff data of Yichang Station and related intervals from 1981 to 2019, 130 tele-correlated climate factors were introduced.The input factors were screened based on partial mutual information, and the LSTM neural network model was used to model and compare the monthly runoff forecasting schemes of Yichang Station.Studies showed that there was a strong correlation among the runoff in the upper reaches of the Changjiang River and the subtropical high pressure in the western Pacific Ocean and El Ni1 o index half a year ago.In addition to tele-correlated climate factors, the runoff at Yichang Station was closely related to the runoff at Yichang Station in the same period last year, and the rainfall in the Minjiang and Tuojiang River, Wujiang River, Yalong River in the previous month.If the rainfall forecast of the Jialing River and the Yibin-Chongqing section in the current month can be applied, the accuracy of long-term runoff forecasting at Yichang Station will be greatly improved.
作者
张海荣
汤正阳
曹辉
ZHANG Hairong;TANG Zhengyang;CAO Hui(Three Gorges Cascade Dispatch&Communication Center,Yichang 443002,China;Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science,Yichang 443002,China)
出处
《人民长江》
北大核心
2022年第10期71-75,共5页
Yangtze River
基金
国家自然科学基金项目(51909010)。
关键词
中长期径流预报
偏互信息
气候因子
LSTM模型
宜昌站
长江流域
medium and long-term runoff forecasting
partial mutual information
climate factor
LSTM model
Yichang Station
Changjiang River Basin
