Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)

The aim of this research is to introduce a novel ensemble approach using Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR), and random forest (RF) models for groundwater-potential mapping (GWPM) in Bastam watershed, Iran. This region suffers from freshwater shortages and the identification of new groundwater sites is a critical need. Remote sensing and geographic information system (GIS) were used to reduce time and financial costs of rapid assessment of groundwater resources. Seventeen physiographical, hydrological, and geological groundwater conditioning factors (GWCFs) were derived from a spatial geo-database. Groundwater data were gathered in field surveys and well-yield data were acquired from the Iranian Department of Water Resources Management for 89 locations with high yield potential values ≥ 11 m3 h−1. These data were mapped in a GIS. From these locations, 62 (70%) were randomly selected to be used for model training, and the remaining 27 (30%) were used for validation of the model. The relative weights of the GWCFs were determined with an RF model. For GWPM, 220 randomly selected points in the study area and their final weights were determined with the VIKOR model. A groundwater potential map was created by interpolating the values at these points using Kriging in GIS. Finally, the area under receiver operating characteristic (AUROC) curve was plotted for the groundwater potential map. The success rate curve (SRC) was computed for the training dataset, and the prediction rate curve (PRC) was calculated for the validation dataset. Results of RF analysis show that land use and land cover, lithology, and elevation are the most significant determinants of groundwater occurrence. The validation results show that the ensemble model had excellent prediction performance (PRC = 0.934) and goodness-of-fit (SRC = 0.925) and reasonably high classification accuracy. The results of this study could aid management of groundwater resources and assist planners and decision makers in groundwater-investment planning to achieve sustainability.

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Groundwater Potential Mapping in Relation to Performance of Boreholes in Weathered/Fractured Basement at Afe Babalola University Ado Ekiti Nigeria

LAUTECH Journal of Civil and Environmental Studies ◽

10.36108/laujoces/0202/40(0120) ◽

2020 ◽

Vol 4 (1) ◽

Author(s):

S.O Oyegoke ◽

A.S Adebanjo ◽

O.O Ayeni ◽

K.O Olowe

Keyword(s):

Groundwater Potential ◽

Yield Potential ◽

Performance Rating ◽

Groundwater Exploitation ◽

Groundwater Yield ◽

Potential Map ◽

Potential Mapping ◽

Groundwater Potential Map ◽

Selection For ◽

Groundwater Potential Mapping

This research was carried out with the aim to check the validity and efficiency of the existing groundwater potential map of Afe Babalola University, Ado Ekiti (ABUAD). The performance rating of the drilled boreholes that spread across the institution was used to validate the groundwater potential map produced by Ademilua and Eluwole in 2013. Forty (40) boreholes that were drilled in the institution were evaluated and overlaid on the existing groundwater potential map with their coordinates serially numbered. From the results obtained, it was observed that in locations designated as having good to moderate groundwater yield (potential) on the map, 21 of drilled boreholes were active, 11 were failed/dry boreholes, meanwhile, for the locations designated as having poor groundwater yield, four of drilled boreholes were active while another four were failed/dry boreholes. This result gives a 62.5% performance rating that the existing groundwater potential map can serve as a useful guide for the purpose of site selection for groundwater exploitation but for optimal usage, an improved map is required.

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Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

Water ◽

10.3390/w12020471 ◽

2020 ◽

Vol 12 (2) ◽

pp. 471 ◽

Cited By ~ 9

Author(s):

Khalid Benjmel ◽

Fouad Amraoui ◽

Said Boutaleb ◽

Mohammed Ouchchen ◽

Amine Tahiri ◽

...

Keyword(s):

Remote Sensing ◽

Groundwater Resources ◽

Local Population ◽

Research Work ◽

Groundwater Potential ◽

Gis And Remote Sensing ◽

Potential Map ◽

Groundwater Potential Map ◽

Hierarchy Process ◽

Basin Area

This research work is intended as a contribution to the development of a multicriteria methodology, combining several factors to control the availability of groundwater resources, in order to optimize the choice of location of future drilling and increase the chances to take water from productive structures which will satisfy the ever-increasing water demand of local population (Arghen basin in the Western Anti-Atlas chain of Morocco). The geographic information system is used to develop thematic maps describing the geometry and the hydrodynamic functioning of the aquifer. In this study, 11 factors including geology, topography, and hydrology, influencing the distribution of water resources were used. Based on the Analytical Hierarchy Process (AHP) model, GIS, and remote sensing, the study mapped and classified areas according to their hydrogeological potential. The favorable potential sectors cover 17% of the total area of the basin. The medium potential sectors account for 64%, while the unfavorable areas cover 18% of the basin area. The groundwater potential map of the study area has been validated by comparing with data from 159 boreholes scattered throughout the basin.

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Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms

Water ◽

10.3390/w13050658 ◽

2021 ◽

Vol 13 (5) ◽

pp. 658

Author(s):

Sadegh Karimi-Rizvandi ◽

Hamid Valipoori Goodarzi ◽

Javad Hatami Afkoueieh ◽

Il-Moon Chung ◽

Ozgur Kisi ◽

...

Keyword(s):

Bayesian Network ◽

Groundwater Resources ◽

Slope Angle ◽

Drainage Density ◽

Groundwater Potential ◽

Estimation Accuracy ◽

Slope Aspect ◽

Khuzestan Province ◽

Potential Mapping ◽

Groundwater Potential Mapping

Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.

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

Applied Sciences ◽

10.3390/app10072469 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2469 ◽

Cited By ~ 17

Author(s):

Phong Tung Nguyen ◽

Duong Hai Ha ◽

Mohammadtaghi Avand ◽

Abolfazl Jaafari ◽

Huu Duy Nguyen ◽

...

Keyword(s):

Logistic Regression ◽

Soft Computing ◽

Predictive Value ◽

Flow Direction ◽

Groundwater Potential ◽

Topographic Wetness Index ◽

Ensemble Techniques ◽

Well Yield ◽

Potential Mapping ◽

Groundwater Potential Mapping

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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Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

Remote Sensing ◽

10.3390/rs13122300 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2300

Author(s):

Samy Elmahdy ◽

Tarig Ali ◽

Mohamed Mohamed

Keyword(s):

Machine Learning ◽

Regional Scale ◽

Regression Trees ◽

Classification And Regression Trees ◽

Groundwater Potential ◽

Machine Learning Algorithms ◽

Conditioning Factors ◽

Potential Mapping ◽

Classification And Regression ◽

Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

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