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

被引:43
作者
Afrifa, Stephen [1 ,2 ]
Zhang, Tao [1 ]
Appiahene, Peter [2 ]
Varadarajan, Vijayakumar [3 ]
机构
[1] Tianjin Univ, Dept Informat & Commun Engn, Tianjin 300072, Peoples R China
[2] Univ Energy & Nat Resources, Dept Comp Sci & Informat, Sunyani 00233, Ghana
[3] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia
关键词
machine learning; mathematical model; statistical model; climate; systematic review; groundwater; groundwater level; CLIMATE-CHANGE; ARTIFICIAL-INTELLIGENCE; TIME-SERIES; PREDICTION; SIMULATION; ALGORITHM; TRANSPORT; IMPACTS; WAVELET;
D O I
10.3390/fi14090259
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页数:31
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