Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability

被引：0
|
作者：
Lu, Dan [1 ]
Yang, Tiantian [2 ]
Liu, Xiaofeng [3 ]
机构：
[1] Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA
[2] Univ Oklahoma, Sch Civil Engn & Environm Sci, Norman, OK USA
[3] Penn State Univ, Dept Civil & Environm Engn, University Pk, PA USA
来源：
FRONTIERS IN WATER | 2023年 / 5卷
关键词：
data-driven machine learning; hydrological and hydraulic predictability; uncertainty; quantification; explainable machine learning; climate change;
D O I：
10.3389/frwa.2023.1215966
中图分类号：
TV21 [水资源调查与水利规划];
学科分类号：
081501 ;
摘要：
引用
收藏
页数：3
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