Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

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ID: 111001
2020
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Abstract
Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.
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nguyen2020appliedsoft Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Phong Tung Nguyen;Duong Hai Ha;Mohammadtaghi Avand;Abolfazl Jaafari;Huu Duy Nguyen;Nadhir Al-Ansari;Tran Van Phong;Rohit Sharma;Raghvendra Kumar;Hiep Van Le;Lanh Si Ho;Indra Prakash;Binh Thai Pham;Nguyen, Phong Tung;Ha, Duong Hai;Avand, Mohammadtaghi;Jaafari, Abolfazl;Nguyen, Huu Duy;Al-Ansari, Nadhir;Van Phong, Tran;Sharma, Rohit;Kumar, Raghvendra;Le, Hiep Van;Ho, Lanh Si;Prakash, Indra;Pham, Binh Thai;
Journal applied sciences
Year 2020
DOI
10.3390/app10072469
URL
https://doi.org/10.3390/app10072469
Keywords
Machine learning bagging ensemble modeling dagging random subspace cascade generalization Machine learning bagging ensemble modeling dagging random subspace cascade generalization

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