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

Chen, Wei; Zhao, Xiao-Ling; Tsangaratos, Paraskevas; Shahabi, Himan; Ilia, Ioanna; Xue, Weifeng; Wang, Xiaojing; Ahmad, Baharin Bin

doi:10.1016/j.jhydrol.2020.124602

Cited by 104 publications

(45 citation statements)

References 70 publications

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“…Scientists have used a variety of computational data mining methods and models in natural hazard research, including studies of floods [18][19][20][21][22][23][24][25][26][27][28], wildfire [29], sinkholes [30], droughtiness [31,32], earthquakes [33,34], land/ground subsidence [35,36], groundwater [21,[37][38][39][40][41][42][43][44], and landslides [22,. These methods extract related patterns in historical data to predict future events [73].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

et al. 2020

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Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

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Section: Introductionmentioning

confidence: 99%

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

et al. 2020

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“…Recent advancement in groundwater potential mapping is the combined use of different machine learning methods towards developing hybrid ensemble machine learning models for obtaining the most accurate results. Chen et al [23] proposed the integrated application of J48 decision trees with the random subspace (RSS), rotation forest (RF), AdaBoost, bagging, and dagging to identify the groundwater potential zones in Wuqi County, China. Naghibi et al [14] improved performances of the BRT, CART, and RF classifiers using a rotation forest ensemble technique for modeling groundwater potential in Meshgin Shahr, Iran.…”

Section: Introductionmentioning

confidence: 99%

“…All these studies demonstrated the enhanced predictive performance of the hybrid ensemble models compared to the single models. In fact, the premise of the application of a hybrid ensemble is that groundwater potential mapping requires big data of various geo-environmental variables [11,12,23,24] that largely make the single modeling approaches inefficient in many regions [29].…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen¹,

Avand

et al. 2020

Applied Sciences

136

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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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“…The main aim of this study is to explore different statistical models, both single and ensemble, to assess spring groundwater potential in a karst area, namely Bojnourd Region, northeast Iran. Although various researchers have tried to delineate groundwater spring potential zones [30,45,103,104], the evaluation of other modeling approaches is highly necessary. To address this, a new ensembling technique is proposed to improve the accuracy of the statistical models and to identify groundwater potential zones.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models

Nhu

Rahmati

Falah

et al. 2020

Water

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Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales.

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

Cited by 104 publications

References 70 publications

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

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

Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models

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