Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model

Zhang，Jiangwei1,2; Liang，Xiuyu1; Zeng，Lingzao3,4; Chen，Xiaohui2; Ma，Enze1; Zhou，Yunqiu1; Zhang，You Kuan1,5

2023-11-01

10.1016/j.jhydrol.2023.130293

Journal of Hydrology   影响因子和分区

0022-1694

1879-2707

Deep learning models exhibit good interpolating ability, but their performance is often hindered by the scarcity of data in groundwater problems. Analytical models (solutions) provide a first-order physical principle for groundwater flow, but they are only applicable under specific conditions, such as when the aquifer is homogeneous. This study introduces a novel framework for deep transfer learning that integrates the strengths of both methods and overcomes their limitations. Specifically, we propose a deep learning model guided by a simple analytical model to predict groundwater flow in heterogeneous aquifers. It differs from previous deep learning model by incorporating the knowledge from the simple analytical model and utilizing transfer learning technique to improve the prediction in relatively complicated problems where the analytical model is not applicable. The model is tested against the traditional deep learning model Deep Back Propagation Neural Network (DBPNN) in the scenarios with unknown hydraulic conductivity fields. The results show that the proposed model significantly improve the accuracy of hydraulic head predictions by fusing analytical knowledge with neural networks. The hydraulic conductivity mainly affects the parameters of the shallow layers in the neural network, which enables the use of transfer learning techniques in more complex problems. In all test scenarios, the prediction errors of the proposed model are much smaller than those of the DBPNN. Additionally, the proposed model performs satisfactorily even with limited training data.

Analytical model Deep learning Groundwater Heterogeneity Transfer learning

SCI ; EI

英语

第一 ; 通讯

National key research and development program[2019YFC1803903] ; National Natural Science Foundation of China["41977165","42172275"] ; Natural Science Foundation of Shenzhen["20220814221815001","JCYJ20190809142203633"] ; Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control[2017B030301012]

Engineering ; Geology ; Water Resources

Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources

WOS:001107520500001

ELSEVIER

20234214921224

Aquifers ; Backpropagation ; Deep learning ; Forecasting ; Groundwater flow ; Groundwater resources ; Hydraulic conductivity ; Hydrogeology ; Learning systems ; Multilayer neural networks

Groundwater:444.2 ; Ergonomics and Human Factors Engineering:461.4 ; Geology:481.1 ; Fluid Flow, General:631.1 ; Hydraulics:632.1 ; Artificial Intelligence:723.4 ; Mathematics:921

ENGINEERING

2-s2.0-85174334488

Scopus

1.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
2.Group of Geo Modelling and Artificial Intelligence,School of Civil Engineering,University of Leeds,Leeds,LS2 9JT,United Kingdom
3.College of Environment and Resource Science,Zhejiang University,Hangzhou,Zhejiang,310058,China
4.Zhejiang Ecological Civilization Academy,Anji,Zhejiang,313399,China
5.Department of Earth and Environmental Sciences,University of Iowa,Iowa City,52242,United States

Zhang，Jiangwei,Liang，Xiuyu,Zeng，Lingzao,et al. Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model[J]. Journal of Hydrology,2023,626.

Zhang，Jiangwei.,Liang，Xiuyu.,Zeng，Lingzao.,Chen，Xiaohui.,Ma，Enze.,...&Zhang，You Kuan.(2023).Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model.Journal of Hydrology,626.

Zhang，Jiangwei,et al."Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model".Journal of Hydrology 626(2023).