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Article

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

1
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
2
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
3
Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro Gwangjin-gu, Seoul 05006, Korea
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Department of Mapping and Surveying, Darya Tarsim Consulting Engineers Co. Ltd., 1457843993 Tehran, Iran
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Department of Multimedia, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
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Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, 46414-356 Iran
*
Authors to whom correspondence should be addressed.
Water 2019, 11(9), 1909; https://doi.org/10.3390/w11091909
Received: 1 August 2019 / Revised: 2 September 2019 / Accepted: 7 September 2019 / Published: 13 September 2019
(This article belongs to the Section Water Resources Management, Policy and Governance)
Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region. View Full-Text
Keywords: springs potential mapping; mixture discriminant analysis; GIS; random forest; linear discriminant analysis springs potential mapping; mixture discriminant analysis; GIS; random forest; linear discriminant analysis
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MDPI and ACS Style

Kalantar, B.; Al-Najjar, H.A.H.; Pradhan, B.; Saeidi, V.; Halin, A.A.; Ueda, N.; Naghibi, S.A. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping. Water 2019, 11, 1909. https://doi.org/10.3390/w11091909

AMA Style

Kalantar B, Al-Najjar HAH, Pradhan B, Saeidi V, Halin AA, Ueda N, Naghibi SA. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping. Water. 2019; 11(9):1909. https://doi.org/10.3390/w11091909

Chicago/Turabian Style

Kalantar, Bahareh; Al-Najjar, Husam A.H.; Pradhan, Biswajeet; Saeidi, Vahideh; Halin, Alfian A.; Ueda, Naonori; Naghibi, Seyed A. 2019. "Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping" Water 11, no. 9: 1909. https://doi.org/10.3390/w11091909

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