Can transfer learning improve hydrological predictions in the alpine regions?-Reference-Cited by-同舟云学术

Can transfer learning improve hydrological predictions in the alpine regions?

Published:2023-10 Issue: Volume:625 Page:130038
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Yao Yingying^ORCID,Zhao Yufeng,Li Xin^ORCID,Feng Dapeng,Shen Chaopeng,Liu Chuankun,Kuang Xingxing,Zheng Chunmiao

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference49 articles.

1. The CAMELS data set: catchment attributes and meteorology for large-sample studies;Addor;Hydrol. Earth Syst. Sci.,2017

2. Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks;Bowes;Water,2019

3. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States;Cai;J. Hydrol.: Reg. Stud.,2021

4. Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method;Cai;J. Hydrol.,2022

5. The ERA5-Land soil temperature bias in permafrost regions;Cao;The Cryosphere,2020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models;Water;2024-07-31

2. Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins;Water Resources Research;2024-07

3. Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions;Water Resources Research;2024-06

4. Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning;Journal of Hydrology;2024-03