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Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping

The main goal of the study was to estimate the efficiency and predictive ability of a series of tree-based ensemble methods in groundwater spring potential mapping. The study introduces a novel hybrid integration approach based on J48 Decision Trees (J48), AdaBoost (AB), Bagging (Bag), RandomSubSpac...

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Main Authors:	Wei, Chen, Xia, Zhao, Tsangaratos, Paraskevas, Shahabi, Himan, Ilia, Ioanna, Xue, Weifeng, Wang, Xiaojing, Ahmad, Baharin
Format:	Article
Published:	Elsevier B. V. 2020
Subjects:	G Geography (General)
Online Access:	http://eprints.utm.my/id/eprint/87136/ http://dx.doi.org/10.1016/j.jhydrol.2020.124602
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id	my.utm.87136
record_format	eprints
spelling	my.utm.871362020-10-31T12:23:45Z http://eprints.utm.my/id/eprint/87136/ Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping Wei, Chen Xia, Zhao Tsangaratos, Paraskevas Shahabi, Himan Ilia, Ioanna Xue, Weifeng Wang, Xiaojing Ahmad, Baharin G Geography (General) The main goal of the study was to estimate the efficiency and predictive ability of a series of tree-based ensemble methods in groundwater spring potential mapping. The study introduces a novel hybrid integration approach based on J48 Decision Trees (J48), AdaBoost (AB), Bagging (Bag), RandomSubSpace (RS), Dagging (Dag) and Rotation Forest (RF) algorithms for constructing a groundwater spring potential map. The performance of the ensemble models were evaluated at the Wuqi County in Shaanxi Province, China. At first, a groundwater spring inventory map with 235 groundwater springs was constructed. The groundwater spring database was randomly divided into a training (70% of the total number of groundwater springs) and validating subset (remaining 30%). Secondly, sixteen groundwater spring related variables were selected and analyzed, such as elevation, slope, aspect, planform and profile curvature, curvature, topographic wetness index, stream transport and stream power indexes, distance from river network, lithology, soil and landuse cover, normalized difference vegetation index, mean annual rainfall and distance from road network. The contribution of each groundwater spring related variable so as to identify the most predictive was based on the correlation attribute evaluation method. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical indexes (accuracy, sensitivity, specificity). The outcomes revealed that all models had a good predictive performance, having AUC values greater than 0.74. Specifically, RF-J48 model appeared with the highest prediction capability (AUC = .0.797), followed by the AB-J48 model (0.793), the Bag-J48 (0.768), the RS-J48 (0.766) and the Dag-J48 (0.748), respectively. The study highlights that the proposed ensemble methodology is efficient and highly accurate appropriate for groundwater spring potential mapping. Elsevier B. V. 2020 Article PeerReviewed Wei, Chen and Xia, Zhao and Tsangaratos, Paraskevas and Shahabi, Himan and Ilia, Ioanna and Xue, Weifeng and Wang, Xiaojing and Ahmad, Baharin (2020) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. Journal of Hydrology, 583 . p. 124602. ISSN 0022-1694 http://dx.doi.org/10.1016/j.jhydrol.2020.124602
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	G Geography (General)
spellingShingle	G Geography (General) Wei, Chen Xia, Zhao Tsangaratos, Paraskevas Shahabi, Himan Ilia, Ioanna Xue, Weifeng Wang, Xiaojing Ahmad, Baharin Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
description	The main goal of the study was to estimate the efficiency and predictive ability of a series of tree-based ensemble methods in groundwater spring potential mapping. The study introduces a novel hybrid integration approach based on J48 Decision Trees (J48), AdaBoost (AB), Bagging (Bag), RandomSubSpace (RS), Dagging (Dag) and Rotation Forest (RF) algorithms for constructing a groundwater spring potential map. The performance of the ensemble models were evaluated at the Wuqi County in Shaanxi Province, China. At first, a groundwater spring inventory map with 235 groundwater springs was constructed. The groundwater spring database was randomly divided into a training (70% of the total number of groundwater springs) and validating subset (remaining 30%). Secondly, sixteen groundwater spring related variables were selected and analyzed, such as elevation, slope, aspect, planform and profile curvature, curvature, topographic wetness index, stream transport and stream power indexes, distance from river network, lithology, soil and landuse cover, normalized difference vegetation index, mean annual rainfall and distance from road network. The contribution of each groundwater spring related variable so as to identify the most predictive was based on the correlation attribute evaluation method. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical indexes (accuracy, sensitivity, specificity). The outcomes revealed that all models had a good predictive performance, having AUC values greater than 0.74. Specifically, RF-J48 model appeared with the highest prediction capability (AUC = .0.797), followed by the AB-J48 model (0.793), the Bag-J48 (0.768), the RS-J48 (0.766) and the Dag-J48 (0.748), respectively. The study highlights that the proposed ensemble methodology is efficient and highly accurate appropriate for groundwater spring potential mapping.
format	Article
author	Wei, Chen Xia, Zhao Tsangaratos, Paraskevas Shahabi, Himan Ilia, Ioanna Xue, Weifeng Wang, Xiaojing Ahmad, Baharin
author_facet	Wei, Chen Xia, Zhao Tsangaratos, Paraskevas Shahabi, Himan Ilia, Ioanna Xue, Weifeng Wang, Xiaojing Ahmad, Baharin
author_sort	Wei, Chen
title	Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
title_short	Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
title_full	Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
title_fullStr	Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
title_full_unstemmed	Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
title_sort	evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping
publisher	Elsevier B. V.
publishDate	2020
url	http://eprints.utm.my/id/eprint/87136/ http://dx.doi.org/10.1016/j.jhydrol.2020.124602
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score	13.211869

Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping

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