Application of machine learning models in groundwater quality assessment and prediction: progress and challenges

doi:10.1007/s11783-024-1789-2

Front. Environ. Sci. Eng.

2024, Vol. 18

Issue (3) : 29 https://doi.org/10.1007/s11783-024-1789-2

REVIEW ARTICLE

Application of machine learning models in groundwater quality assessment and prediction: progress and challenges

Yanpeng Huang^1,², Chao Wang^2,³, Yuanhao Wang³, Guangfeng Lyu³, Sijie Lin³, Weijiang Liu⁴, Haobo Niu⁵, Qing Hu^2,³(

)

¹. School of Environment, Harbin Institute of Technology, Harbin 150090, China
². School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
³. Engineering Innovation Center of SUSTech (Beijing), Southern University of Science and Technology, Beijing 100083, China
⁴. Technical Center for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China
⁵. Chinese Academy of Environmental Planning, Beijing 100043, China

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Abstract

● The application of ML in groundwater quality assessment and prediction is reviewed.

● Bibliometric analysis is performed and summarized to promote application.

● The details of the application of ML in GQAP are comprehensively summarized.

● Challenges and opportunities of using ML models in GQAP are discussed.

Groundwater quality assessment and prediction (GQAP) is vital for protecting groundwater resources. Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding. Recently, the application of machine learning (ML) in GAQP (GQAPxML) has been widely studied due to ML’s reliability and efficiency. While many GQAPxML publications exist, a thorough review is missing. This review provides a comprehensive summary of the development of ML applications in the field of GQAP. First, the workflow of ML modeling is briefly introduced, as are data preparation, model development, model evaluation, and model application. Second, 299 publications related to the topic are filtered, mainly through ML modeling. Subsequently, many aspects of GQAPxML, such as publication trends, the spatial distribution of study areas, the size of data sets, and ML algorithms, are discussed from a bibliometric perspective. In addition, we review in detail the well-established applications and recent findings for several subtopics, including groundwater quality assessment, groundwater quality modeling using groundwater quality parameters, groundwater quality spatial mapping, probability estimation of exceeding the groundwater quality threshold, groundwater quality temporal prediction, and the hybrid use of ML and physics-based models. Finally, the development of GQAPxML is explored from three perspectives: data collection and preprocessing, model building and evaluation, and the broadening of model applications. This review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.

Keywords Groundwater quality assessment Groundwater quality prediction Machine learning Groundwater modeling

Corresponding Author(s): Qing Hu

About author:

Peng Lei and Charity Ngina Mwangi contributed equally to this work.

Issue Date: 31 October 2023

Cite this article:

Yanpeng Huang,Chao Wang,Yuanhao Wang, et al. Application of machine learning models in groundwater quality assessment and prediction: progress and challenges[J]. Front. Environ. Sci. Eng., 2024, 18(3): 29.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1789-2
https://academic.hep.com.cn/fese/EN/Y2024/V18/I3/29

Tab.1 Keywords used for literature retrieval in this study

Fig.1 Model confusion matrix for the test set.

Fig.2 Growth in total publications involving GQAPxML and GWxML. The inset shows the subtopic results within GQAPxML.

Fig.3 Distribution of study areas with GQAPxML publications: (a) Top 13 countries by number of publications and (b) temporal trend for the top 4 countries based on number of publications.

Fig.4 Changes in the average annual sample size and number of explanatory variables.

Tab.2 Ranking of explanatory variable frequency in GQAPxML-related publications (top 40)

Tab.3 Ranking of the response variable frequency in GQAPxML-related publications (top 20)

Fig.5 Machine learning problem type distribution in 299 publications.

Fig.6 The algorithms applied in the studies. (a) Frequency of studies using different numbers of algorithms, (b) frequency of each algorithm reported as optimal.

Fig.7 Frequency of different data set division ratios.

Fig.8 The evaluation index used for machine learning evaluation in 299 publications. (a) Frequency statistics for different numbers of evaluation indexes used and (b) evaluation index frequency.

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