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Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds

JI Huiping CHEN Yaning FANG Gonghuan LI Zhi DUAN Weili ZHANG Qifei

干旱区科学2021，Vol.13Issue(6)：549-567,19.

Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds

JI Huiping ¹CHEN Yaning ²FANG Gonghuan ¹LI Zhi ²DUAN Weili ¹ZHANG Qifei²

作者信息

1. State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China
2. University of Chinese Academy of Sciences,Beijing 100049,China
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摘要

关键词

hydrological simulation/long short-term memory/extreme gradient boosting/support vector regression/SWAT_Glacier model/Tianshan Mountains

Key words

hydrological simulation/long short-term memory/extreme gradient boosting/support vector regression/SWAT_Glacier model/Tianshan Mountains

引用本文复制引用

JI Huiping,CHEN Yaning,FANG Gonghuan,LI Zhi,DUAN Weili,ZHANG Qifei..Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds[J].干旱区科学,2021,13(6):549-567,19.

基金项目

This research was supported by the National Natural Science Foundation of China(U1903208,41630859,42071046).The authors wish to express great thanks to Prof.YANG Jing from National Institute of Water and Atmospheric Research in New Zealand for his guidance on hydrological models. （U1903208,41630859,42071046）

干旱区科学

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ISSN：1674-6767

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