A comparison of machine learning models for the mapping of groundwater spring potential

Al-Fugara, A’kif; Pourghasemi, Hamid Reza; Al‐Shabeeb, Abdel Rahman; Habib, Maan; Al‐Adamat, Rida; Al-Amoush, Hani; Collins, Adrian L.

doi:10.1007/s12665-020-08944-1

Cited by 47 publications

(15 citation statements)

References 67 publications

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“…Deep boosting takes advantage of the data analyses and the favorable learning ability [28], which are the reasons for its superior performance in generating a GW potential map. On the other hand, boosted regression trees also produced high accuracy maps, which could be due to the fact that they keep important GW conditioning factors, detect the interactions, model distinct kinds of factors, and finally, manage missing data [17,88]. Their acceptable efficiency is in line with the previous studies [7,17,69].…”

Section: Discussionsupporting

confidence: 72%

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

et al. 2020

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Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

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Section: Discussionsupporting

confidence: 72%

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

et al. 2020

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“…These techniques rely on the concept that systems Sustainability 2021, 13, 2459 2 of 19 can learn from data, recognize patterns, and make choices with the least human intervention [10]. Various machine learning techniques, such as artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis, quadratic discriminant analysis, the k-nearest neighbor algorithm, multivariate adaptive regression splines, and decision trees [11][12][13][14][15][16][17], have widely been adopted for the analysis and mapping of groundwater potential.…”

Section: Introductionmentioning

confidence: 99%

“…In boosting, multiple decision trees are grown sequentially using information from existing trees [21]. The random forest (RF) and gradient boosting machine (GBM), which are representative algorithms for bagging and boosting, respectively, have been extensively applied for groundwater potential analysis [11][12][13][14][15][16][17][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

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Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

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“…Algorithms used in the GPM literature include Mixture Discriminant Analysis (Al-Fugara et al, 2020), Random Forest (Kalantar et al, 2019;Moghaddam et al, 2020), Boosted Regression Tree (Naghibi et al, 2016), Logistic Regression (Ozdemir, 2011;Chen et al, 2018;Nhu et al, 2020), Support Vector Machines (Naghibi et al, 2017b), Neural Networks (Lee et al, 2012;Panahi et al, 2020) and Ensemble methods (Naghibi et al, 2017a;Martínez-Santos and Renard, 2020;Nguyen et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Preprocessing approaches in machine learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

Gómez-Escalonilla

Martínez‐Santos

Martín-Loeches

2021

Preprint

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Abstract. Groundwater is crucial for domestic supplies in the Sahel, where the strategic importance of aquifers can only be expected to increase in the coming years due to climate change. Groundwater potential mapping is gaining recognition as a valuable tool to underpin water management practices in the region, and hence, to improve water access. This paper presents a machine learning method to map groundwater potential and illustrates it through an application to two regions of Mali. A set of explanatory variables for the presence of groundwater is developed first. Several scaling methods (standardization, normalization, maximum absolute value and min-max scaling) are used to avoid the pitfalls associated with the reclassification of explanatory variables. A number of supervised learning classifiers is then trained and tested on a large borehole database (n = 3,345) in order to find meaningful correlations between the presence or absence of groundwater and the explanatory variables. This process identifies noisy, collinear and counterproductive variables and excludes them from the input dataset. Tree-based algorithms, including the AdaBoost, Gradient Boosting, Random Forest, Decision Tree and Extra Trees classifiers were found to outperform other algorithms on a consistent basis (accuracy > 0.85), whereas maximum absolute value and standardization proved the most efficient methods to scale explanatory variables. Borehole flow rate data is used to calibrate the results beyond standard machine learning metrics, thus adding robustness to the predictions. The southern part of the study area was identified as the better groundwater prospect, which is consistent with the geological and climatic setting. From a methodological standpoint, the outcomes lead to three major conclusions: (1) because there is no aprioristic way to know which algorithm will work better on a given dataset, we advocate the use of a large number of machine learning classifiers, out of which the best are subsequently picked for ensembling; (2) standard machine learning metrics may be of limited value when appraising map outcomes, and should be complemented with hydrogeological indicators whenever possible; and (3) the scaling of the variables helps to minimize bias arising from expert judgement and maintains robust predictive capabilities.

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A comparison of machine learning models for the mapping of groundwater spring potential

Cited by 47 publications

References 67 publications

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Preprocessing approaches in machine learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

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