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

Huang，Yanpeng1,2; Wang，Chao2,3; Wang，Yuanhao3; Lyu，Guangfeng3; Lin，Sijie3; Liu，Weijiang4; Niu，Haobo5; Hu，Qing2,3

2024-03-01

10.1007/s11783-024-1789-2

Frontiers of Environmental Science and Engineering   影响因子和分区

2095-2201

2095-221X

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.[Figure not available: see fulltext.]

Groundwater modeling Groundwater quality assessment Groundwater quality prediction Machine learning

SCI ; EI

英语

通讯

2-s2.0-85179178818

Scopus

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

Huang，Yanpeng,Wang，Chao,Wang，Yuanhao,et al. Application of machine learning models in groundwater quality assessment and prediction: progress and challenges[J]. Frontiers of Environmental Science and Engineering,2024,18(3).

Huang，Yanpeng.,Wang，Chao.,Wang，Yuanhao.,Lyu，Guangfeng.,Lin，Sijie.,...&Hu，Qing.(2024).Application of machine learning models in groundwater quality assessment and prediction: progress and challenges.Frontiers of Environmental Science and Engineering,18(3).

Huang，Yanpeng,et al."Application of machine learning models in groundwater quality assessment and prediction: progress and challenges".Frontiers of Environmental Science and Engineering 18.3(2024).