Groundwater Potential Model Research Articles

Groundwater potential mapping using machine learning approach in West Java, Indonesia

Groundwater availability is a challenge as it is utilized for vital sectors such as agricultural sector, human consumption, and industrial sector. Therefore, water resource mapping is needed to be performed to maintain water resource sustainability. This research aims to investigate groundwater potential in West Java, Indonesia using supervised machine learning (ML) methods namely Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Several groundwater conditioning factors were used in this research such as Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, geomorphology, land use land cover (LULC), soil type, and land system. The groundwater potential prediction model was validated using the groundwater potential map and well locations obtained from the Ministry of Energy and Mineral Resources and the Ministry of Public Works and Public Housing of Republic of Indonesia, respectively. The results show that the highest overall accuracy was achieved using RF method (0.8). We found that the land system was the highest contributor to groundwater potential mapping (25%), followed by lithology (16%), NDVI (15%), geomorphology and TWI (14% each), and LULC and soil type (8% each). More than 50% of the West Java Province region exhibited groundwater potential in very low and low classes, while the high and very high classes of groundwater potential were only less than 16%. Ground geoelectric measurements were conducted in sample areas in Bandung City and Sukabumi District, representing very high and very low groundwater potentials, respectively. This study emphasizes the critical need to implement measures that ensure the sustainability of water resources and prevent mismanagement of groundwater extraction, particularly in West Java.

Enhancing groundwater potential zone mapping with a hybrid analytical method: The case of semiarid basin

Groundwater resource management is a critical component of sustainable water use, necessitating accurate and nuanced mapping of groundwater potential zones. This study analyzed the groundwater potential of Algeria's 43,750 km2 Chelif Basin (more than 17% of the area of Northern Algeria) using a combination of both subjective and objective mapping techniques. The adopted approaches encompass the benchmark analytical Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) methodologies to quantify interdependencies of criteria related to groundwater potential. The analysis focused on ten criteria related to groundwater potential, including core moisture availability and key hydrological factors like distance to river, topographic wetness index, and hydrological soils. Fuzzy-AHP achieved slightly higher groundwater prospecting accuracy (AUC = 0.730) than classic AHP (0.716), with benchmarking against 15 related studies indicating robust performance. Instead of the most commonly used criteria in groundwater literature such as lineament, stream order, recharge rate, and drainage density, this study employed alternative factors to challenge and validate the efficacy of the proposed methodology. The decision to omit certain criteria facilitated a more focused and manageable analysis, yet still delivered a robust evaluation of groundwater potential in the studied area. Moreover, this approach underscores the adaptability of the proposed methodology to accommodate varying sets of criteria, tailored according to the availability of data and specific research objectives. Additionally, the DEMATEL evaluation reveals new insights into subtle prioritization divergences, specifically between domain specialist opinions and analytical assessments of the criteria. The integration of fuzzy logic and causal relationship mapping through DEMATEL provides a comprehensive and robust foundation for groundwater potential modeling.

Groundwater potential modelling and aquifer zonation of a typical basement complex terrain: a case study

Groundwater potential modelling and aquifer zonation of a typical basement complex terrain: a case study

Groundwater potential modeling over the eastern part of Ghana’s Northern Region using evidence belief functions and weight of evidence

Groundwater potential modeling over the eastern part of Ghana’s Northern Region using evidence belief functions and weight of evidence

Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.

Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.

Geophysical Approach for Groundwater Resource Assessment: A Case Study of Oda Community Akure, Southwestern Nigeria

The geophysical investigation for groundwater was carried out at Oda town, Akure south local government area of Ondo State. Fourteen (14) points were sounded using a Schlumberger array with AB/2 of 80 m and the resulting geoelectric parameters were used in the estimation of the aquifer layer parameters of the subsurface. The first layer resistivity value ranges from 29 to 164 Ωm and thickness ranges from 0.6 to 2.5 m. The second layer has a resistivity value ranging from 21-1361 Ωm with a thickness ranging from 1.5 m to 14.6 m. The third layer resistivity value is from 68 to 297 Ωm with thickness ranging from 4 m to 12.4 m. The fourth layer which is the deepest layer has a resistivity value ranging from 180 to 4364 Ωm with depth ranging from 4 m to 19.5 m. The parameters interpreted from the geoelectric data were used to generate the aquifer thickness and resistivity maps, with bedrock relief which were combined to produce the groundwater potential map of the area. These maps were used to characterise the study area into low to high groundwater potential zones. The southwestern and eastern parts were identified as productive groundwater potential zones. The result was validated by taking water column depth from eight existing hand-dug wells. A significant correlation was obtained between the groundwater potential model and the well water column. The surveyed area is generally suitable for hand-dug well aside from the north-eastern part where groundwater potential is low and water volume is observed.

GROUNDWATER POTENTIAL ASSESSMENT OF PART OF KAZAURE, NIGERIA USING ERDAS IMAGINE, PCI GEOMATICA, ARCMAP AND EXPERT CHOICE SOFTWARE

The possibility of utilizing groundwater as a source of water supply for public use is always attractive This research was focused to evaluate the groundwater potential of Kazaure area, Nigeria. The methodology adopted were mainly primary data in which six different thematic maps were integrated to generate the groundwater potential model (GWPM. It was produced by Weighted Linear Combination (WLC) where each class individual’s weight was multiplied by the map scores. In the research, Erdas Imagine 9.2, PCI Geomatica 10.0, ArcMap 9.3 and Expert Choice 10.0 in a GIS environment were used. Vertical electrical sounding (VES) was conducted using Ohmega resistivity meter with Schlumberger configuration. IP12win software was used in plotting the curves which revealed the resistivity, layers and thickness of the VES stations. Geological mapping was carried out using topographical map on a scale of 1:50,000Measurement of the depth to groundwater table in hand dug wells was carried out using dip meter. Water samples were collected and analyzed. Atomic Absorption Spectrometry (AAS) and X-Ray Fluorescence (XRF) were used in the analysis of water quality at the Multi-user laboratory, Chemistry Department, Ahmadu Bello University Zaria, Nigeria.

Drought: Profile Assessment, Impact Analysis and Coping Strategies to Combat Drought in Naogaon District of Bangladesh

This article tends to analyse the drought scenario of the Naogaon district in Bangladesh through groundwater potential zone modelling. The study’s findings illustrate that the maximum area covers a moderately potential groundwater zone. The study data also portrays various threats, including food insecurity and economic, social and environmental risks that were noticed as a consequence of the severe drought. The findings from the data indicate that the Porsha and Sapahar upazillas (sub-districts) of Naogaon district are more vulnerable zones in terms of drought. It is noteworthy that the people have been using indigenous knowledge to combat drought hazards. Local people are using crop calendars, agronomic management, water harvesting practices for less water-consuming crops and changes in occupation to manage the risks of drought in the study region.

GIS-based multi-criteria decision-making techniques and analytical hierarchical process for delineation of groundwater potential.

The profound knowledge and management of groundwater resources is a prerequisite in order to ensure the sustainability of these resources. In this research, multi-criteria decision-making (MCDM) and analytical hierarchical process (AHP) method based on GIS were used to determine the groundwater potential recharge model of the Mornag plain (coastal area in North Tunisia). Influential eight factors were used in the groundwater recharge modeling: lithology, land use/land cover, hydrogeomorphology, elevation, rainfall, drainage density, lineament density, and soil. The influence of each factor was examined by the weighted linear combination method. The results reveal a very high to high groundwater recharge potential in the order of 20% of the total area. The validation of results by the histogram method showed that 41% of the total area corresponds to the moderate to very high recharge potential classes. The groundwater recharge potential model (GIS-MCDM-AHP) is useful in better management and planning of groundwater resources and implementation of wells and hydraulic structures in arid and semi-arid areas.

Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm

Today, water supply in order to achieve sustainable development goals is one of the most important concerns and challenges in most countries. For this reason, accurate identification of areas with groundwater potential is one of the important tools in the protection, management and exploitation of water resources. Accordingly, the present study was conducted with the aim of modeling and predicting groundwater potential in Markazi province, Iran using Multivariate adaptive regression spline (MARS) and Support vector machine (SVM) machine learning models and using two random search (RS) and Bayesian optimization hyperparameter algorithms to optimize the parameters of the SVM model. For this purpose, 18 variables affecting the groundwater potential and 3482 spring locations were used to model the groundwater potential. Data for modeling were divided into two categories of training (70%) and validation (30%). The receiver operating characteristics (ROC) were used to evaluate the performance of the models. The results of evaluation models showed that using hyperparameters random search and Bayesian optimization were improved SVM accuracy in training and validation stages. Bayesian optimization methods are very efficient because they are consciously choosing the parameters of the model that this strategy improves the performance of the model. Evaluating accuracy in the validation stage showed that the AUC value is for MARS, SVM, RS-SVM and B-SVM models 87.40%, 88.25%, 90.73% and 91.73%, respectively. The results of assessment variables importance showed elevation, precipitation in the coldest month, soil and slope variables have the most importance in modeling groundwater potential, while aspect, profile curvature and TWI variables, have the least importance in predicting groundwater potential in Markazi province.

A multi-criteria approach of assessing groundwater potential, recharge and flow in the transboundary Tuli Karoo Basin

Abstract Quantitative estimates of amount of groundwater resources are required in the transboundary Tuli Karoo Basin to better manage and promote sustainable conjunctive use of the aquifer. Unfortunately, such important data and information are lacking. The aim of this study was to map groundwater potential zones and model groundwater recharge as well as groundwater flow in the Tuli Karoo Basin (12,164 km2) using geospatial techniques. To delineate groundwater potential zones, nine thematic maps of groundwater conditioning factors were computed and weighted using Saaty's Analytical Hierarchy Process (AHP). To validate the groundwater potential map, a Kruskal–Wallis test was performed. Groundwater recharge and groundwater flow direction were estimated in a coupled Geographic Information System (GIS) and modelling environment using the soil–water–balance model (SWB) and British Geological Survey (BGS) groundwater tool, respectively. Results for groundwater potential mapping showed that the area is dominated by high ground water potential which covers about 57.8% (6,915.1 km2) of the basin. The Kruskal–Wallis test showed that the median values of the borehole yields vary significantly between the different groundwater potential zone classes (P-value <0.0001). Estimated groundwater recharge using SWB model showed that the maximum annual potential recharge for the Tuli Karoo Basin was 13.2 mm/year for the 10-year period from 2010 to 2020. Results from the BGS tool for groundwater flow show that the dominant groundwater flow direction is southeast. The results showed that there is no link between groundwater flow direction and surface water flow direction. This study is relevant in water development policy, water-related development aid, community planning and technical decision making for hydrogeologists, catchment managers, water planners and non-specialists alike.

Groundwater potential zonation using VES and GIS techniques: A case study of Weserbi Guto catchment in Sululta, Oromia, Ethiopia

Groundwater is an appreciated and vital natural resource in the world, and it is of the utmost essential for the growth and development of a country. Nevertheless, assessing the groundwater potential and its recharge region is still ambiguous due to the nature of groundwater. In this study, the groundwater potential of the Weserbi Guto Laga Qawe Catchment Sululta area was assessed using VES and GIS methods. For the model, thematic layers were generated from the geophysical investigation, existing maps, and field survey results and were integrated into the GIS environment to delineate the groundwater potential zones. Factors such as lineament density, drainage density, elevation or topography, slope gradient, aquifer resistivity, and lithology were derived, reclassified, and scaled to common ranges and assigned with appropriate weights. The groundwater potential zonation model of the site was produced by the multicriteria evaluation method. Accordingly, the geometrical interval method was utilized to classify the index into three zones (high, moderate, and low) to produce the map. The model result revealed that a large part of the study area fell into the high zone with 50.14 % (3669.99ha). whereas 35.85% (262.72ha) and 14.01% (1024.95ha) show moderate and low groundwater potential, respectively. The resulting map was validated using eleven existing water level data points and the result was found to be in good agreement with the model.

Spatial prediction of groundwater potentiality using machine learning methods with Grey Wolf and Sparrow Search Algorithms

• Four hybrid models were constructed for groundwater potential mapping and different sample datasets (D1–D3) were used to increase the confidence of the result. • The Sparrow Search Algorithm was first used in groundwater modeling. • The correlation between conditioning factors and groundwater occurrence were analyze. • The hybrid models were validated using ROC curve and related statistical indexes. • All hybrid models obtained high accuracy on the training and validation datasets, and the Sparrow Search Algorithm performs better. Groundwater is considered to be one of the most valuable natural resources in the world. However, the availability of groundwater is of concern. Therefore, understanding the potential of groundwater is very important for the utilization of water resources. The main goal of the study was to predict and assess the groundwater using hybrid machine learning and metaheuristic algorithms to automatically tune the parameters, namely the Random Forest (RF), Support Vector Machines (SVM), the Grey Wolf Algorithm (GWO), and the Sparrow Search Algorithm (SSA). A total of 608 groundwater locations were identified by field surveys. Three different sample datasets (D1-D3) were created to increase the confidence of the result, and each dataset was divided randomly into a training set (70%) and a validation set (30%). Fifteen conditioning factors involving geology, human activity, and hydrology were extracted from the available materials. The Evidential Belief Function (EBF) was employed to determine the correlation between groundwater and factors. Then fourteen relevant factors were selected by feature selection. After that, the hybrid models of RF-GWO, RF-SSA, SVM-GWO, and SVM-SSA built using the datasets (D1-D3) were applied to generate groundwater potential maps (GPMs). Results showed that the performances of the hybrid models can be considered to be stable. The global performance of these hybrid models was assessed using the area under the receiver operating characteristic curve (AUC-ROC) and related statistical indexes. According to the D1 dataset validation results, the AUC values for the RF-GWO, RF-SSA, SVM-GWO, and SVM-SSA were 0.832, 0.840, 0.790, and 0.809, respectively. The RF-SSA had the highest accuracy (0.764), with an AUC of 0.840, and the SVM-GWO showed the least accuracy (0.723), with an AUC of 0.790. The outcomes revealed that all hybrid models had a good predictive performance. However, the RF outperformed the SVM model, and the SSA algorithm is superior to the GWO algorithm. Overall, the results of this study show that the construction of the groundwater potential model using a metaheuristic optimization algorithm is a feasible approach.

Integrating spatially explicit sensitivity and uncertainty analysis in a multi-criteria decision analysis-based groundwater potential zone model

This study presents a spatially explicit sensitivity and uncertainty analysis approach to a GIS-based multi-criteria groundwater potential zone model. The study addressed a deficiency in the way groundwater potential mapping results are typically presented using discrete class outputs without assessment of their certainty with respect to variations in criteria weighting, one of the main contributors to output uncertainty in GIS-based multi-criteria decision analysis studies. We argue, moderating groundwater potential mapping results with localised uncertainty levels will help to refine and prioritise groundwater exploration efforts. The approach also enables a better understanding of the underlying factors influencing uncertainty in model outputs, which can help to inform the calibration of input parameters to improve model performance. Although the procedures presented in this study have been applied to other types of multi-criteria evaluations, its integration in GIS-based groundwater potential modelling has received little attention. We provide a case study focused on a fractured rock environment surrounding the township of Hawker in South Australia where new groundwater resources are sought. Small incremental weight changes were applied one-at-a-time and automated as a task in ArcGIS Pro, built using the ArcPy Python module that interacts with spatial tools allowing geographical analysis. The approach is applicable to both continuous and discrete class-based mapping outputs and enabled a deeper understanding of model output behaviour with respect to criteria weighting alternatives. The case study findings demonstrate the potential value of the approach in mitigating uncertainty and improving confidence in locating sites with high groundwater potential.

Groundwater potential mapping of Tawi River basin of Jammu District, India, using geospatial techniques.

The demand for water is increasing around the world due to population growth, urbanization, industrialization, etc., which is making groundwater a vital natural resource for meeting the growing demand for water. According to the central groundwater report, Jammu district has adequate groundwater potential (GWP) and comes under the safe category. However, the GWP has not been fully utilized, thereby leading to a water shortage in the district. Therefore, this study has been designed to examine the GWP zones in the Tawi River basin of Jammu district using geospatial techniques. For this, several GWP conditioning parameters, such as elevation, slope, geology, geomorphology, rainfall, soil, land use/land cover, topographic wetness index (TWI), drainage density, lineament density, roughness, and curvature, were utilized. The analytical hierarchy process (AHP) technique was used to evaluate the weights of the selected criteria after a pair-wise comparison of each criterion with the rest of the criteria. The result showed that the high GWP zone accounts for 21.98% of the area, the moderate zone covers an area of 40.54%, the low GWP area accounts for about 34.91%, and only 2.57% of the area lies under the very low GWP zone. The validation of GWP zones using 25 monitoring wells showed an accuracy of 80% in GWP modeling. The findings of this study may be utilized in meeting the rising demand for water in the region.

Aquifer evaluation in parts of north-central Nigeria from geo-electrical derived parameters

In order to reduce the level of risk associated with borehole drilling, it is important to have detailed knowledge about the aquifer distribution. In a view to generating groundwater potential model of Lokoja and its environs, the detailed subsurface characterization was carried out using a GIS-based multi-criteria decision analysis approach. One hundred and twenty-four vertical electrical sounding (VES) data points were covered within the study area using the Schlumberger array of electrical resistivity surveys. Hydrogeological investigation of one hundred and twenty-four existing boreholes within the vicinity of the sounded VES points was carried out by measuring in situ parameters of each borehole such as borehole depth, elevation, static water level and borehole yield. A Multi-Criteria Decision Analysis-Analytical Hierarchy Process (MCDA)-AHP-based was carried out by estimating the probabilistic ratings for the classes of parameters used for modeling groundwater potential. Four groundwater potential influencing factors, namely coefficient of anisotropy, transverse resistance, aquifer resistivity and aquifer thickness, were classified and rated. The output of the multi-criteria decision analysis was processed in the GIS environment to produce a groundwater potential index map. The obtained model was validated by comparing it with in situ borehole yield data to determine the accuracy of the proposed model. The groundwater potential map generated classified the study area into low, medium and high yield zones. Areas with medium potential zones dominate the largest part of the with 66% area coverage, and the dominance of these zones was visible in the northern and western part of the study area. Areas with high groundwater potential exist toward the southern and eastern sections of the study area. This area was observed to be underlined with sandstone, siltstone and migmatite. The validation exercise carried out on the proposed model reveals a 70% prediction accuracy.

AN INTEGRATED APPROACH TO GROUNDWATER POTENTIAL MODELLING IN JOS METROPOLIS, NORTH CENTRAL NIGERIA.

Groundwater resources are unevenly distributed in the crystalline basement terrain of Nigeria. Hence, the sustainable development and management of groundwater resource in such terrain requires precise quantitative assessment based on scientific principle and modern techniques. This study deals with the integrated approach of remote sensing, GIS and Analytical Hierarchy Process (AHP) in modelling groundwater potential zones within Jos, Northcentral Nigeria. Nigeria Sat - X, ASTER GDEM, topographic and geological maps of the study area are used to generate the various themes; lineaments, geology, drainage, geomorphology, surface water body and slope. Thematic weights are assign to the various layers AHP. The groundwater potential model of the area is produce by integrating the different thematic layers using Weighted Index Overlay Analysis (WIOA) in ArcGIS. Five different potential zones namely; very high, high, moderate, low and very low are determined. The groundwater potential map reveals that some parts in the northwestern, parts of the northeastern and a portion in the extreme southwestern of the study area have very low potential. Low potential is observed in the extreme northwestern, some areas in the northeast and some southwestern parts of the study area. The north central, some northwestern and northeastern as well as southern parts generally exhibits moderate potential. The high and very high groundwater potentiality of the study area is restricted to some parts in the northwest, northeast and few places in the central and southwestern part of the study area. Borehole data obtained from 127 boreholes within the study area where used to validate the groundwater potential of the study area, from which 69.29% coincided with the groundwater potential model.

Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed

Due to numerous droughts in recent years, the amount of surface water in arid and semi-arid regions has decreased significantly, so reliance on groundwater to meet local and regional demands has increased. The Kabgian watershed is a karst watershed in southwestern Iran that provides a significant proportion of drinking and agriculture water supplies in the area. This study identified areas with karst groundwater potential using a combination of machine learning and statistical models, including entropy-SVM-LN, entropy-SVM-SG, and entropy-SVM-RBF. To do this, 384 karst springs were identified and mapped. Sixteen factors that are related to karst potential were identified from a review of the literature, and these were compiled for the study area. The 384 locations were randomly separated into two categories for training (269 location) and validation (115 location) datasets to be used in the modeling process. The ROC curve was used to evaluate the modeling results. The models used, in general, were good at determining the location of karst groundwater potential. The evaluation showed that the E-SVM-RBF model had an area under the curve of 0.92, indicating that it was most accurate estimator of groundwater potential among the ensemble models. Evaluation of the relative importance of each of the 16 factors revealed that land use, a vector ruggedness measure, curvature, and topography roughness index were the most important explainers of the presence of karst groundwater in the study area. It was also found that the factors affecting the presence of karst springs are significantly different from non-karst springs.

Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping