Comparison Between Machine Learning and Bivariate Statistical Models for Groundwater Recharge Zones

被引：0

作者：

Bilal Aslam ^{[1
]}

Ahsen Maqsoom ^{[2
]}

Usman Hassan ^{[3
]}

Sidra Maqsoom ^{[4
]}

Wesam Salah Alaloul ^{[5
]}

Muhammad Ali Musarat ^{[5
]}

Muhammad Shahzaib ^{[3
]}

Muhammad Irfan ^{[6
]}

机构：

[1] Department of Earth Sciences, Quaid-i-Azam University, Islamabad

[2] Green Tech Institute, University Mohammed VI Polytechnic, Benguerir

[3] Department of Civil Engineering, COMSATS University Islamabad Wah Campus, Wah Cantt

[4] Department of Construction Engineering and Infrastructure Management, Asian Institute of Technology, Khlong Luang

[5] Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Perak, Bandar Seri Iskandar

[6] Civil Engineering Department, HITEC University, Taxila

来源：

Iranian Journal of Science and Technology, Transactions of Civil Engineering | 2025年 / 49卷 / 1期

关键词：

Bivariate statistical models; Data mining techniques; Geographic information system (GIS); GPM; Hybrid models;

D O I：

10.1007/s40996-024-01721-1

中图分类号：

学科分类号：

摘要：

Due to population growth and climate change, dependence on groundwater is expected to increase. This growth has put forth a major challenge for management for sustainable groundwater storage. This study illustrates a newly introduced bivariate statistical model with an ensembled data mining approach. Certainty factors (CF), evidential belief function (EBF), frequency ratio (FR) and convolutional neural network (CNN) are four bivariant statistical models. These four models are integrated with the logistic model tree (LMT) and random forest (RF). These models are used for preparing the groundwater potential map (GPM). The receiver operating characteristic (ROC) curve and area under the curve (AUC) were utilized for calculating the accuracy of the groundwater potential maps. The sequence and values of AUC obtained from the results are as CNN-RF (0.923), CF-RF (0.914), EBF-RF (0.911), FR-RF (0.904), CF-LMT (0.893), EBF-LMT (0.872) and FR-LMT (0.817). It can be concluded that the combination of bivariate statistic models and data mining techniques advance the method’s efficiency in creating a potential mapping of groundwater. © The Author(s), under exclusive licence to Shiraz University 2025.

引用

页码：853 / 876

页数：23

共 92 条

[1]

Abd Manap M., Nampak H., Pradhan B., Lee S., Sulaiman W.N.A., Ramli M.F., Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS, Arab J Geosci, 7, 2, pp. 711-724, (2014)

[2]

Abdalla F., Mapping of groundwater prospective zones using remote sensing and GIS techniques: a case study from the Central Eastern Desert, Egypt, J Afr Earth Sci, 70, pp. 8-17, (2012)

[3]

Aburub F., Hadi W.F., Predicting groundwater areas using data mining techniques: groundwater in Jordan as case study, Int J Comput Inf Eng, 10, 9, pp. 1621-1624, (2016)

[4]

Adiat K., Nawawi M.K., Abdullah K.M., Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool—a case of predicting potential zones of sustainable groundwater resources, J Hydrol, 440, pp. 75-89, (2012)

[5]

Al-Abadi A.M., Groundwater potential mapping at northeastern Wasit and Missan governorates, Iraq using a data-driven weights of evidence technique in framework of GIS, Environ Earth Sci, 74, 2, pp. 1109-1124, (2015)

[6]

Alatorre L.C., Sanchez-Andres R., Cirujano S., Begueria S., Sanchez-Carrillo S., Identification of mangrove areas by remote sensing: the ROC curve technique applied to the northwestern Mexico coastal zone using Landsat imagery, Remote Sens, 3, 8, pp. 1568-1583, (2011)

[7]

Arabameri A., Rezaei K., Cerda A., Lombardo L., Rodrigo-Comino J., GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches, Sci Tot Environ, 658, pp. 160-177, (2019)

[8]

Arabgol R.S., Sartaj M., Asghari K., Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model, Environ Model Assess, 21, 1, pp. 71-82, (2016)

[9]

Ayazi M.H., Pirasteh S., Arvin A., Pradhan B., Nikouravan B., Mansor S., Disasters and risk reduction in groundwater: Zagros Mountain Southwest Iran using geoinformatics techniques, Disaster Adv, 3, 1, pp. 51-57, (2010)

[10]

Bhat S., Motz L.H., Pathak C., Kuebler L., Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA, Environ Monitor Assess, 187, 1, (2015)

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