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

Nguyen, Phong Tung; Ha, Duong Hai; Avand, Mohammadtaghi; Jaafari, Abolfazl; Nguyen, Huu Duy; Al‐Ansari, Nadhir; Phong, Tran Van; Sharma, Rohit; Kumar, Raghvendra; Le, Hiep Van; Ho, Lanh Si; Prakash, Indra; Pham, Binh Thai

doi:10.3390/app10072469

Cited by 129 publications

(41 citation statements)

References 92 publications

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“…Naghibi and Pourghasemi (2015) indicate that the importance of explanatory variables in groundwater potential mapping is considerably influenced by the approach used in an investigation and the study area properties. Outcomes shows that elevation, rainfall, geology and drainage density, among others, are the most important factors conditioning the groundwater potential, which is in agreement with results obtained by other authors in different geographical contexts (Ozdemir, 2011;Naghibi and Pourghasemi, 2015;Nguyen et al, 2020b).…”

Section: Model Evaluation and Scaling Methods Selectionsupporting

confidence: 92%

“…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%

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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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Section: Model Evaluation and Scaling Methods Selectionsupporting

confidence: 92%

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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“…Nhu et al [ 55 ] coupled a reduced pruning error tree model with AB, Bagging, and Random Subspace techniques for gully erosion susceptibility mapping using in the Shoor River watershed of Iran. Nguyen et al [ 61 , 62 ] proposed ensemble modeling based on the ANN and logistic regression for groundwater potential mapping in two different regions of Vietnam.…”

Section: Discussionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

Nhu

Mohammadi

Shahabi

et al. 2020

IJERPH

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We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

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“…Distance to rivers is one of the important factors related to adjacent ground slope erosion and saturation causing landslides [81][82][83]. e drainage in the area is structurally controlled (faults/lineaments).…”

Section: Distance To Riversmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

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

Cited by 129 publications

References 92 publications

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

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

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

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