随机森林
期限(时间)
接头(建筑物)
环境科学
相关系数
学位(音乐)
水位
水文学(农业)
气象学
统计
计算机科学
数学
工程类
机器学习
土木工程
岩土工程
地理
地图学
物理
量子力学
声学
作者
Nannan Du,Xuechun Liang,Congyou Wang,Jia Lu
标识
DOI:10.1109/ispds56360.2022.9874178
摘要
In order to solve the problem of low accuracy of long-term water level forecasting, a multi-station joint long-term water level forecasting model combining random forest and Informer was proposed. First, the Pearson correlation coefficient (PCC) between hydrological stations is calculated, and the hydrological station with the highest degree of correlation with the water level of Hongze Lake is found; then, the random forest (RF) is used to re-extract and select the hydrological station index; finally, the RF and Informer are combined. The experimental results show that the proposed model has higher prediction accuracy.
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