分解
分水岭
地表径流
联轴节(管道)
理论(学习稳定性)
航程(航空)
系列(地层学)
水文学(农业)
水文模型
计算机科学
环境科学
数据挖掘
地质学
气候学
机器学习
岩土工程
工程类
机械工程
生态学
古生物学
生物
航空航天工程
作者
Gongbo Ding,Chao Wang,Xiaohui Lei,L. Xue,Hao Wang,Xinhua Zhang,Peibing Song,Yi Jiang,Ruifang Yuan,Ke Xu
标识
DOI:10.3389/feart.2023.1185953
摘要
Widely confirmed and applied, data-driven models are an important method for watershed runoff predictions. Since decomposition methods such as time series decomposition cannot automatically handle the decomposition process of date changes and less consideration of influencing factors before decomposition, resulting in insufficient correlation analysis between influencing factors and forecast objects, we propose a method based on hydrological model decomposition to generate time series state variables (broadening the range of influencing factors to be considered). In this study, we constructed hydrological models wherein rainfall and other hydrological elements are decomposed into hydrological and hydrodynamic characteristic state variables to expand the range of the prediction factors. A data-driven model was then built to perform runoff predictions in the Han River Basin. The results showed that compared with the single prediction model, the prediction results based on the coupling model were superior, the performance evaluation grade of the coupling model was high, and the coupling model had a higher stability.
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