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Open AccessArticle

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Center for Advanced Modeling and Geospatial System (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, Building 11, 81 Broadway, Sydney, NSW 2007, Australia
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Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843-2117, USA
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College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
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School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
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Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
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Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
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Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
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Authors to whom correspondence should be addressed.
Water 2019, 11(10), 2013; https://doi.org/10.3390/w11102013
Received: 20 July 2019 / Revised: 21 September 2019 / Accepted: 22 September 2019 / Published: 27 September 2019
(This article belongs to the Special Issue Characterizing Groundwater - Surface Water Interaction Using GIS)
This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources. View Full-Text
Keywords: groundwater modeling; ensemble model; over-fitting; performance; Chilgazi watershed; Iran groundwater modeling; ensemble model; over-fitting; performance; Chilgazi watershed; Iran
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MDPI and ACS Style

Tien Bui, D.; Shirzadi, A.; Chapi, K.; Shahabi, H.; Pradhan, B.; Pham, B.T.; Singh, V.P.; Chen, W.; Khosravi, K.; Bin Ahmad, B.; Lee, S. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. Water 2019, 11, 2013. https://doi.org/10.3390/w11102013

AMA Style

Tien Bui D, Shirzadi A, Chapi K, Shahabi H, Pradhan B, Pham BT, Singh VP, Chen W, Khosravi K, Bin Ahmad B, Lee S. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. Water. 2019; 11(10):2013. https://doi.org/10.3390/w11102013

Chicago/Turabian Style

Tien Bui, Dieu; Shirzadi, Ataollah; Chapi, Kamran; Shahabi, Himan; Pradhan, Biswajeet; Pham, Binh T.; Singh, Vijay P.; Chen, Wei; Khosravi, Khabat; Bin Ahmad, Baharin; Lee, Saro. 2019. "A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping" Water 11, no. 10: 2013. https://doi.org/10.3390/w11102013

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