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

Cai，Hejiang1,2,3; Liu，Suning4; Shi，Haiyun2,3; Zhou，Zhaoqiang2,3; Jiang，Shijie5; Babovic，Vladan1

2022-10-01

10.1016/j.jhydrol.2022.128495

JOURNAL OF HYDROLOGY   Impact Factor & Quartile

0022-1694

1879-2707

Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.

Catchment scale Deep learning Groundwater level predictions Water balance mechanism

SCI

English

Corresponding

[51909117] ; [JCYJ20210324105014039]

Engineering ; Geology ; Water Resources

Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources

WOS:000868341800002

ELSEVIER

ENGINEERING

2-s2.0-85139037756

Scopus

1.Department of Civil and Environmental Engineering,National University of Singapore,Singapore
2.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,China
3.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,China
4.Center for Climate Physics,Institute for Basic Science,Busan,South Korea
5.Department of Computational Hydrosystems,Helmholtz Centre for Environmental Research,Leipzig,Germany

Cai，Hejiang,Liu，Suning,Shi，Haiyun,et al. Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method[J]. JOURNAL OF HYDROLOGY,2022,613.

Cai，Hejiang,Liu，Suning,Shi，Haiyun,Zhou，Zhaoqiang,Jiang，Shijie,&Babovic，Vladan.(2022).Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method.JOURNAL OF HYDROLOGY,613.

Cai，Hejiang,et al."Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method".JOURNAL OF HYDROLOGY 613(2022).