Title: | Adaptability of machine learning methods and hydrological models to discharge simulations in datasparse glaciated watersheds |
Authors: | Ji, H., Y. Chen, G. Fang, Z. Li, W. Duan and Q. Zhang |
Year: | 2021 |
Journal: | Journal of Arid Land |
Volume (Issue): | 13(6) |
Pages: | 549–567 |
Article ID: | |
DOI: | 10.1007/s40333-021-0066-5 |
URL (non-DOI journals): | |
Model: | SWAT_Glacier & SWAT |
Broad Application Category: | hydrologic only |
Primary Application Category: | model comparison |
Secondary Application Category: | snowmelt, frozen soil and/or glacier melt processes |
Watershed Description: | 63,000 km^2 Aksu River, which is a tributary of the Tarim River, and 12,900 km^2 Kumaric River; both are located in northwest China. |
Calibration Summary: | |
Validation Summary: | |
General Comments: | |
Language: | English |
Keywords: | hydrological simulation; long short-term memory; extreme gradient boosting; support vector regression;
SWAT_Glacier model; Tianshan Mountains |