Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq

Al-Abadi, Alaa M.; Fryar, Alan E.; Rasheed, Arjan A.; Pradhan, Biswajeet

doi:10.1007/s12665-021-09725-0

Cited by 22 publications

(6 citation statements)

References 57 publications

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“…Slope is the magnitude of inclination of a surface in reference to a horizontal plane that affects water flow under the influence of gravity, thereby determining subsurface lateral transmissivity rate (Bhattacharya et al 2021;Al-Abadi et al 2021). It controls the quantity of water that collects in a certain area, and hence plays an essential role in groundwater recharge.…”

Section: Slopementioning

confidence: 99%

“…We chose those variables, which many researchers have extensively used. In the plain regions, topographic and climatic parameters have been recognized as important variables, while in the mountains, along with topographic, geological variables have been described as critical variables for GWP mapping (Mallick et al 2021c;Al-Djazouli et al 2021;Pathak et al 2021;Namous et al 2021;Al-Abadi et al 2021). For example, drainage density could be a valid variable in flood plains, not in mountainous regions (Bhattacharya et al 2021;Fadhillah et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Novel hybrid models to enhance the efficiency of groundwater potentiality model

et al. 2022

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The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.

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Section: Slopementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel hybrid models to enhance the efficiency of groundwater potentiality model

et al. 2022

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“…The recent literature showcases several examples of ML algorithms in groundwater potential mapping studies. These include support vector machines (Al-Fugara et al, 2022;Panahi et al, 2020), decision trees (Al-Abadi et al, 2021;Arabameri et al, 2021;Braham et al, 2022), artificial neural networks (Chen et al, 2021;Nguyen et al, 2020;Hakim et al, 2022), and ensemble methods like boosting, random forests and extra trees classification, among others (Bai et al, 2022;Choudhary et al, 2022;Gómez-Escalonilla et al, 2021, 2022Martinsen et al, 2022;Sachdeva and Kumar, 2021).…”

Section: Introductionmentioning

confidence: 99%

Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers

Gómez-Escalonilla

Diancoumba²,

Traoré³

et al. 2022

Journal of Hydrology: Regional Studies

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“…Some researchers have used groundwater potential analysis as an exploration tool by assuming that the study of surficial factors such as topography, geology, geomorphology, soil, land use/land cover (LULC), drainage characteristics, lineament density, and proximity to surface-water bodies offer an indirect exploration tool to find where the groundwater is more likely to occur [10][11][12]. Others have contended that groundwater potential maps show variation in groundwater storage across a given region and thus provide information on groundwater availability and productivity [5,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…In data-knowledge models, such as simple overlay technique, fuzzy logic, and multi-criteria decision making (MCDM), a specific number of groundwater-affecting occurrence factors are combined to generate the groundwater potential map [16][17][18][19]. In data-driven models, such as bivariate and multivariate statistical models, ML classifiers, and hybrids of these three models, or with knowledge-driven models, the relationship between the locations of wells with specified pumping capacity or specific capacity (Sc) and the groundwater influential occurrence factors is explored to map groundwater potential [5,13,[20][21][22][23][24]. The locations of the operating groundwater wells, in this case, are taken as the target (dependent) variable and the groundwater influential occurrence factors are taken as predictors (independent variable).…”

Section: Introductionmentioning

confidence: 99%

Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq

Al-Ozeer

Al-Abadi

Hussain

et al. 2021

Water

Self Cite

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Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.

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Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq

Cited by 22 publications

References 57 publications

Novel hybrid models to enhance the efficiency of groundwater potentiality model

Novel hybrid models to enhance the efficiency of groundwater potentiality model

Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers

Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq

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