Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method-Reference-Cited by-同舟云学术

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Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method

Published:2022-10 Issue: Volume:613 Page:128495
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Cai Hejiang,Liu Suning,Shi Haiyun,Zhou Zhaoqiang,Jiang Shijie,Babovic Vladan

Funder

Natural Science Foundation of Shenzhen City

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Medicine

Reference67 articles.

1. The CAMELS data set: catchment attributes and meteorology for large-sample studies;Addor;Hydrology and Earth System Sciences,2017

2. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: a case study for six basins from the CAMELS dataset;Ayzel;Comput. Geosci.,2021

3. Introducing knowledge into learning based on genetic programming;Babovic;Journal of Hydroinformatics,2009

4. Rainfall runoff modelling based on genetic programming;Babovic;Nordic Hydrology,2002

5. Soil Functions: Connecting Earth's Critical Zone;Banwart;Annual Review of Earth and Planetary Sciences,2019

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