Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Stephen Afrifa (), Tao Zhang, Peter Appiahene and Vijayakumar Varadarajan
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Stephen Afrifa: Department of Information and Communication Engineering, Tianjin University, Tianjin 300072, China
Tao Zhang: Department of Information and Communication Engineering, Tianjin University, Tianjin 300072, China
Peter Appiahene: Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani 00233, Ghana
Vijayakumar Varadarajan: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia

Future Internet, 2022, vol. 14, issue 9, 1-31

Abstract: With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

Keywords: machine learning; mathematical model; statistical model; climate; systematic review; groundwater; groundwater level (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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