Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

Al-Fugara, A’kif; Ahmadlou, Mohammad; Al‐Shabeeb, Abdel Rahman; AlAyyash, Saad; Al-Amoush, Hani; Al‐Adamat, Rida

doi:10.1080/10106049.2020.1716396

Cited by 64 publications

(27 citation statements)

References 42 publications

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“…Avand et al [25] integrated the best first decision tree with the AdaBoost, multiboosting, and bagging ensembles for groundwater potential mapping in the Yasuj-Dena area, Iran. Al-Fugara et al [26] developed a hybrid model based on the SVM and genetic algorithm (GA) for groundwater potential mapping in Jerash and Ajloun, Jordan. In another study, Naghibi et al [19] used GA for optimizing the structure of the SVM and RF models for groundwater potential mapping in Iran.…”

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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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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“…Miraki et al [14] developed an ensemble model (RS-RF) using a combination of RF and Random Subspace ensemble technique to assess the groundwater potential in the Qorveh-Dehgolan plain, Kurdistan province, Iran, and reported that the RS-RF model is a promising tool for mapping of groundwater potential. Al-Fugara et al [15] combined Support Vector Machine (SVM) and Genetic Algorithm (GA) to build a hybrid model for mapping groundwater potential in the Jerash and Ajloun region of Jordan. Naghibi et al [16] used Adaboost, Bagging, Generalized Additive to optimize Naïve Bayes for better performance of groundwater potential modeling.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

107

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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“…Genetic algorithms (GA) are the ideal option for heuristic unsupervised classification. Heuristic unsupervised classification is based on the creation of some mathematical designs and, subsequently, the improvement of a predefined indicator to determine the cluster information and the centroids mechanically [93]. Heuristic optimization procedures are considered to be a reproducible, accurate, and time-efficient method of repeatedly categorising remote sensing images.…”

Section: Change Detectionmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

Cited by 64 publications

References 42 publications

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

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

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Detection of Urban and Environmental Changes via Remote Sensing

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