Groundwater Potential Map Research Articles

A Multidisciplinary Approach for Groundwater Potential Mapping in a Fractured Semi-Arid Terrain (Kerdous Inlier, Western Anti-Atlas, Morocco)

This study is focused on developing an approach for spatial mapping of groundwater by considering four types of factors (geological, topographical, hydrological, and climatic factors), and by using different bivariate statistical models, such as frequency ratio (FR) and Shannon’s entropy (SE). The developed approach was applied in a fractured aquifer basin (Ameln Basin, Western Anti-Atlas, Morocco), to map the spatial variation of groundwater potential. Fifteen factors (15) influencing groundwater were considered in this study, including slope degree, slope aspect, elevation, topographic wetness index (TWI), slope length (LS), topographic position index (TPI), plane curvature, profile curvature, drainage density, lineament density, distance to rivers and fault network, normalized difference vegetation index (NDVI), lithology, and land surface temperature (LST). The potential maps produced were then classified into five classes to illustrate the spatial view of each potential class obtained. The predictive capacity of the frequency ratio and Shannon’s entropy models was determined using two different methods, the first one based on the use of flow data from 49 boreholes drilled in the study area, to test and statistically calibrate the predictive capacity of each model. The results show that the percentage of positive water points corresponds to the most productive areas (high water flow) (42.86% and 30.61% for the FR and SE models, respectively). On the other hand, the low water flows are consistent with the predicted unfavorable areas for hydrogeological prospecting (4.08% for the FR model and 6.12% for the SE model). Additionally, the second validation method involves the integration of 7200 Hz apparent resistivity data to identify conductive zones that are groundwater circulation zones. The interpretation of the geophysical results shows that the high-potential zones match with low apparent resistivity zones, and therefore promising targets for hydrogeological investigation. The FR and SE models have proved very efficient for hydrogeological mapping at a fractured basement area and suggest that the northern and southern part of the study area, specifically the two major fault zones (Ameln Valley in the north, and the Tighmi-Tifermit Valley in the south) has an adequate availability of groundwater, whereas the central part, covering the localities of Tarçouat, Boutabi, Tililan, and Ighalen, presents a scarcity of groundwater. The trend histogram of the evolution of positive water points according to each potentiality class obtained suggests that the FR model was more accurate than the SE model in predicting the potential groundwater areas. The results suggest that the proposed approach is very important for hydrogeological mapping of fractured aquifers, and the resulting maps can be helpful to managers and planners to generate groundwater development plans and attenuate the consequences of future drought.

Sustainable Groundwater Potential Zoning with Integrating GIS, Remote Sensing, and AHP Model: A Case from North-Central Bangladesh

Groundwater is one of the most valuable natural resources, and the most dependable source of fresh water. For sustainable groundwater management, the present study aimed to model groundwater potential zones in the north–central region of Bangladesh using GIS, remote sensing, and the analytical hierarchy process. The present study included eight thematic layers: lineament density, geomorphology, soil types, slope, land use/land cover, drainage density, elevation, and rainfall features to delineate a groundwater potential zone of the area. Integration of the eight thematic layers was performed through weighted overlay analysis, which assisted in delineating groundwater potential zones. This simple and systematic method successfully provides a satisfactory result concerning the delineation of groundwater potential zones. The study resulted in a groundwater potential zone map, which identifies about 11.51% of the study area as being under a very high groundwater potential zone, covering an area of 504.09 km2. The AHP analysis shows that the physiographical parameters, such as lineament density, slope, and drainage density, and meteorological factors such as annual rainfall, have greater influence over groundwater potentiality. The result obtained from the weighted overlay analysis was verified with actual well yield and groundwater depth data, which show a significant positive correlation. The outcome of the study will help in taking effective measures to ensure sustainable use and extraction of groundwater in this region.

Groundwater potential characterisation over the Voltaian basin using geophysical, geological, hydrological and topographical datasets

The delineation of prospective zones of groundwater resources has been carried out over the Voltaian basin as a viable alternative source of potable water resource to complement increasing demand for domestic and industrial consumption, owing to the danger posed to surficial water resources within the basin by various anthropogenic activities. In achieving this objective, the groundwater potential of the area was assessed by generating a groundwater potential map (GPM) derived by integrating nine (9) evidential layers obtained from geological, geophysical, hydrological, and topographical datasets in geospatial setting using geographic information systems (GIS). The integration of these nine evidential layers was preceded by the assignment of a weight to each evidential layer based on the fuzzy analytical hierarchy process (FAHP) multi-criteria decision making (MCDM) technique. The assignment of weights to these 9 layers using the FAHP approach was done by three decision-makers (DMs) and dwelled on their relative relevance to groundwater resource delineation within the Voltaian basin. The three DMs employed in weighting these layers have expertise in applying geophysical, geological, hydrological and topographical datasets in groundwater prospecting. The weights obtained for the various evidential layers were subjected to normalisation and subsequently consolidated to produce the GPM using a fuzzy gamma operator value of 0.9 in GIS. The efficiency of the GPM was tested using the Nash-Sutclliffe efficiency (NSE) test and the Index of Agreement test. In all, 0.095%, 10.57%, 44.879%, 40.973%, and 3.974% of the study area were delineated as very high, good, moderate, poor, and very poor groundwater zones, respectively by the GPM. The NSE test yielded a value of 0.9996, and the Index of Agreement produced a value of 0.9999. In this regard, results obtained show that FAHP-based model produced is efficient and subsequently good for groundwater prediction over the Voltaian basin.

Spatial mapping of groundwater potentiality applying ensemble of computational intelligence and machine learning approaches

Groundwater plays a significant role in satisfying the human need, and it regulates the earth's hydrological system. Due to increase in population, the stress on groundwater has been successively increased. First step for managing the groundwater is the identification of groundwater potential areas with sound methods. Malda district has high agricultural dependence and in turn huge amount of groundwater has been extracted for irrigation and drinking. As a result the problems of water scarcity for drinking and other purposes has aggravated in this study area. Main aim of this research article is to prepare spatial groundwater potential maps (GWPMs) of Malda district of India using hybrid ensemble of computational intelligence and machine learning approaches including Bagging, artificial neural network (ANN)-Bagging, random forest (RF) and support vector machine (SVM). Total 93 well locations and 17 groundwater potentiality determining factors (GWPDFs) were selected. GWPDFs were selected based on the rank correlation and multi-collinearity analysis. The contribution of GWPDFs was assessed by the RF and sensitivity analysis. As per these methods distance from the river, rainfall, lithology are the most important factors in determining the potentiality of groundwater. The robustness and accuracy of the four groundwater potential maps were evaluated by the nine statistical methods. GWPMs showed that only 23.41% area of this district is very highly potential for groundwater. All the statistical methods used for testing the accurateness of the GWPMs including receiver operating characteristics (ROC), accuracy, etc. acknowledged the significant results of the groundwater potential models. Among the selected models ANN-Bagging ensemble model achieved the highest accuracy (area under curve = 0.96) followed by Bagging, RF and SVM. Thus, the present study ensured that ANN-Bagging ensemble method might be used as an important tool for identifying groundwater potential area that could be helpful to land use planners and local people for groundwater management.

Delineation of groundwater potential zones in KwaZulu-Natal, South Africa using remote sensing, GIS and AHP

The present study aimed to spatially identify groundwater potential (GWP) zones in KwaZulu-Natal (KZN), South Africa (SA) using Geographic Information Systems (GIS) and remote sensing-based multi-criteria analysis. This study is the first to utilise these tools in delineating groundwater in KZN, which is a process dominated by outdated, expensive, and time-consuming in-situ methods. Remote sensing images were used instead of traditional data sampling, which provided a cheaper, faster, and sufficient way of acquiring specific information in data-scarce KZN. Remotely sensed spatial thematic layers viz. geology, lineament density, slope, drainage density, rainfall, land use/land cover, and evapotranspiration were processed and developed using GIS and weighted using the Analytical Hierarchy Process (AHP). The thematic layers were subsequently aggregated using the GIS Weighted Overlay Method to develop a groundwater potential index map. Indices from the map were correlated with 113 borehole logs using the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC) to validate the results. The resultant GWP map computed that approximately 47.3 Km2 (2%) of the total area falls under Excellent GWP, 24405.4 Km2 (27.45%) under good GWP, 50950.5 Km2 (57.3%) under moderate GWP, and the poor and very poor GWP zones constitute around 13380.8 Km2 (15.1%) and 135.6 Km2 (1%) of KZN. The spatial mapping revealed that the AHP-based GWP map exhibited a very good correlation with borehole data (AUC = 72.6%, n = 113), indicating the accuracy of the AHP as a rating method. The use of freely available remote sensing data and modeling techniques provided a relatively inexpensive groundwater identification method that can successfully replace the rudimentary methods.

Groundwater Potential Zone Assessment using GIS and Remote Sensing

Over-exploitation of groundwater and marked changes in climate over the years have imposed immense pressure on the global groundwater resources. As demand of water increases across the globe for human consumption, agriculture and industrial uses, the need to evaluate the groundwater potential and productivity of aquifers also increases. This paper aimed to delineate the groundwater potential zones using GIS and remote sensing. Overlay analysis technique is used to develop the groundwater potential prospect zones by integrating different groundwater contributing thematic layers. The thematic layers of land use and land cover, drainage density, lineament density, soil, geology, slope and rainfall were prepared and used for groundwater potential map development by assigning weights to each thematic layer and features. The weights of each thematic layer were assigned based on their importance and relationship with groundwater recharge. Finally, the thematic maps were integrated by Raster calculator tool to develop groundwater potential zones.

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.

Application of Geoinformatics with Frequency Ratio (FR) Model to Delineate Different Groundwater Potential Zones in Ken Basin, India

The goal of present study was to make groundwater potential zones map using the frequency ratio (FR) model (i.e., probabilistic based bivariate statistical approach) and Geoinformatics technique in Ken Basin, India. Very few studies have been done yet to demarcate the groundwater potential zones using FR model in India. This research contains the analysis of spatial relationships between groundwater and its contributing factors viz., geology, geomorphology, lineament density, land use/ land cover, soil texture, rainfall, slope, drainage density, depth to water level – pre monsoon and depth to water level – post monsoon. The ten groundwater contributing factors were collected from different resources and prepared in 10 m spatial resolution in ArcGIS 10.8.There are 425 observation wells located in the study area, out of these, 296 (70%) observation wells were randomly selected as training datasets for the model and the remaining 129 (30%) were used as a training dataset for the validation purpose. The final output of groundwater potential zones map was classified into five different zones as very high, high, moderate, low, and very low. A more significant portion of the Ken Basin, nearly 30.66%, falls within the moderate groundwater potential zone followed by poor and good potential zones. For quantitative validation, ROC curve analysis was executed through the IBM SPSS software by comparing existing groundwater well locations in the validation datasets (well yield data) with the groundwater potential map obtained by the FR model. The validation results illustrated that the area under the curve (AUC) for the frequency model is 78.1% which ratio achieved fair satisfactory prediction accuracy. The outcome of this study is highly reliable which can serve as a guideline for the planning of exploration, exploitation, and sustainable management of groundwater resources in the study area.

Groundwater Potentiality Assessment of Ain Sefra Region in Upper Wadi Namous Basin, Algeria Using Integrated Geospatial Approaches

Water demand has been increasing considerably around the world, mostly since the start of the COVID-19 pandemic. It has caused many problems for water supply, especially in arid areas. Consequently, there is a need to assimilate lessons learned to ensure water security. In arid climates, evaluating the groundwater potential is critical, particularly because the only source of drinking water and irrigation for the community is groundwater. The objective of this report is to locate and identify probable groundwater basins in the upper Wadi Namous basin’s Ain Sefra area. GIS and RS were used to evaluate the parameters of morphometry and to demarcate groundwater potential zones by using eight different influencing factors, viz., geology, rainfall, height, slope, land cover, land use, and lineaments density are all factors to consider. The analytical hierarchical process (AHP) was used to give weightages to the factors, and definitions within each attribute were sorted in order of priority for groundwater potentiality. The major findings of the research were the creation of groundwater-potential zones in the watershed. The hydrogeological zone of the basin was assessed as follows: very poor (0.56%), poor (26.41%), moderate (44.72%), good (25.22%), and very good (3.1%). The groundwater recharge potential zones are concentrated in low cretaceous locations, according to analytical data. The groundwater potential regions were checked to field inventory data from 45 water locations to corroborate the findings. The qualitative findings and the groundwater inventory data agreed 77.78%, according to the cross-validation study. The produced groundwater potential map might substantially assist in the development of long-term management plans by enabling water planners and decision-makers to identify zones appropriate for the placement of productive wells and reducing investment losses caused by well drilling failures. The results of the study will also serve as a benchmark for further research and studies, such as hydrogeological modeling.

Identification of groundwater potential zones using remote sensing, GIS, machine learning and electrical resistivity tomography techniques in Guelma basin, northeastern Algeria

In this research we assess and map groundwater potential in the Guelma Basin (northeastern Algeria) using an approach combining remote sensing, GIS, statistical and machine learning models. Four models were used including the frequency ratio model with both conventional (CFR) and modified (MFR) versions, the decision tree (DT), and the random forest (RF). For this purpose, firstly, thirteen hydro-geo-morphological variables influencing groundwater potential have been mapped using GIS and remote sensing techniques including elevation, slope, aspect, topographic wetness index, slope Length and Steepness factor, profile curvature, plan curvature, drainage density, distance to river, lineament and fault density, distance to faults and lineaments, lithology, and land use/land cover. Secondly, the groundwater potential was assessed and mapped based on the four models using the training data. Finally, the obtained groundwater potential maps of the four models have been validated using two approaches: (i) a statistical approach based on the receiver operating characteristics curves (ROC); (ii) a geophysical approach by interpreting the electrical resistivity tomography (ERT) results. The validation process gives the Random Forest method as the most accurate. The obtained map by this model is the main finding of this research, where the very high groundwater potential class occupies 8.25%. It is located mostly in the Guelma plain centre and in the northern part of the study area. The used approach and the obtained results may serve for water resource managers to improve groundwater resource planning and to resolve regional scale issues in this area or elsewhere.

Assessing and delineation of groundwater recharge areas in coastal arid area southern Tunisia

In Gabès region (southeastern Tunisia), given the semi-arid to arid climate conditions, groundwater is an essential resource to supply the growth needs of the socio-economic development. To ensure sustainable development and preserve water resources, a careful estimation of the present day recharge amount and the delineation of the potential zones of rainfall precipitation are of required for an accurate evaluation of regional water balance. In this context, this study aims to a preliminary assessment of groundwater recharge in Gabes basin in regard to the delineation of the potential recharge areas of phreatic aquifers. Thus, a geological and hydrogeological collected database coupled with remote sensing techniques (RST) were used for the determination of the lateral variation of recharging zone, Treatment by ArcGIS and Matlab softwares and Kohonen self-organizing maps (K-SOM) approaches. The obtained results indicate that five recharge potential areas have been identified and classified as 27% very low, 23% low, 40% moderate recharge, 7% good and 3% very good potential recharge located principally on southern part of the study region. This distribution is controlled principally by the geomorphological, geologic, and hydrogeologic features of the region. Reasonable management strategies based on a perennial exploitation of these low renewable resources are required to optimize the water dependent socio-economic development. The estimated groundwater potential recharge of Gabès aquifer system using K-SOM and RST is of 11.4 Mm3 y−1. This recharging rate is very low it present 7% of the total rain, thus it must be ameliorated. K-SOM and RST approach is a useful method for groundwater potential recharge mapping and is a helpful of wells establishment and groundwater sustainable management. an isotopic analyses is recommended to ameliorate the decision maker to establish the adequate strategy.

Impact of climate change on groundwater potential and recharge in the drought prone Runde catchment of Zimbabwe

Abstract Groundwater provides critical freshwater supplies for most rural communities living in drought-prone areas. Such is the case for Runde catchment in Zimbabwe, whose rural communities depend on groundwater. Climate change and increased variability pose a threat to water availability by affecting groundwater potential and recharge, but the full extent of the threat is not well understood. Thus, the main objective of this study was to assess the impact of climate change on groundwater potential and recharge in the catchment. The groundwater potential mapping was performed using a spatially weighted overlay method with inputs: soil type, geology, land use, observed precipitation, topographic wetness index and elevation. This mapping produced a groundwater potential index, classified into groundwater potential zones and cross-validated with borehole yield data, r=0.63 and n=62. The groundwater potential validation showed 1.6 and 4.8% of the total boreholes were in the high (>7 L/s) to very high (4–7 L/s) while 43.5 and 50.1% moderate (1–4 L/s), and low (<1 L/s) groundwater potential zones respectively. The simulated precipitation increased by 23% for 2020–2080. Climate change impacts decreased average groundwater potential by 30.8% (13,062.90 km2) low, 5.8% (2433.25 km2) moderate and increased by 34.8% (14,707 km2) high by 1.8% (789.15 km2) very high groundwater potential. For sustainable groundwater management, a holistic approach informs climate change adaptation and mitigation policies.

Identification of groundwater potential zones in southern India using geospatial and decision-making approaches

The present study was carried out to identify of the groundwater potential zones (GWPZ) in the northern part of the Anantapur district of Andhra Pradesh State, India using Remote Sensing (RS), Geographical information system (GIS), and Analytical Hierarchy Process (AHP) approaches. In this study, various thematic maps categorized viz. geomorphology (GM), lineament density (LD), drainage density (DD), geology, land use/land cover (LULC), soils, slope, and rainfall for assessment of GWPZs, which is generated using RS and GIS technique. Furthermore, the relative weights were allocated to various thematic maps using the AHP approach and the relative rank assigned to each sub-criterion based on expert advice. The combination of the eight thematic layers in ArcGIS resulted in a groundwater potential map, providing the information about very good 2.45% (87.06 km2), good 12.76 (452.56 km2), moderate 63.47% (2250.75 km2), poor 15.99% (567.16 km2), and very poor 5.32% (188.73 km2) groundwater possible zones. The acquired outcomes were validated with the area under the curve (AUC/ROC) method. The results show that there is a strong positive correlation between the GWPZs with 78% validation high performance and decreases to the low yield potential with poor areas. This study concludes that the AHP model will be a more reliable for the assessment of the GWP. Any groundwater management project carried out in these favourable regions would benefit the stack holders.

Combined geospatial, geophysical and hydrochemical studies on coastal aquifer at Muttom–Mandaikadu area, Tamilnadu, India

A study was conducted in the Muttom-Mandaikadu coastal region, which is among the profitable coastal sectors in Tamil Nadu, to find the groundwater potential as well as its quality by an integrated geospatial, geophysical and geochemical approach. The GIS-based weighted overlay analysis was used to merge five thematic layers to create the groundwater potential zone map. The geophysical resistivity survey was performed in the study area at 26 stations by applying Schlumberger vertical electrical sounding technique. The observed data were inverted to develop a subsurface lithology model and its electrical properties using one-dimensional software AGI Earth Imager. The combined vertical electrical sounding result and remote sensing thematic maps have exposed the potential zone of groundwater in the study area. From the inferred results, it was observed that 20.8% of the area has ample groundwater potential and 7.7% of the area has scanty groundwater potential. The saltwater intrusion zone had been predicted by validating aquifer resistivity with Dar-Zarrouck (D-Z) parameter. From the geophysical and geochemical interpreted results, it was found that aquifers in 34.6% of the study area are vulnerable to saline contamination. The 4-D model with integrated groundwater quantity and quality suggests that the study area's Western part falls under excellent-to-good groundwater potential zone and excellent water quality.

Efficiency of Geospatial Technology and Multi-Criteria Decision Analysis for Groundwater Potential Mapping in a Semi-Arid Region

The increasing water demand in Egypt causes massive stress on groundwater resources. The high variability in the groundwater depth, aquifer properties, terrain characteristics, and shortage of rainfall make it necessary to identify the groundwater potentiality in semi-arid regions. This study used the possibilities of multi-criteria decision approaches (MCDA), geographical information system (GIS), and groundwater field data to delineate potential groundwater zones in the Tushka area, west of Lake Nasser, South Egypt. Furthermore, groundwater potentiality identification can help decision-makers better plan and manage the water resources in this promising area. Eight controlling factors were utilized to achieve the objective of the present work using multi-criteria decision analysis (MCDA) approaches, namely the analytical hierarchy process (AHP) and frequency ratio (FR) models. The controlling parameters were integrated with the geographic information system (GIS) to develop the zones of groundwater potentialities. The results revealed that high and moderate-potential zones cover approximately 61% and 52% of the total area in the AHP and FR models, respectively. A total of 44 groundwater production wells along with the well yield were collected and used to validate the models. The results were evaluated using the receiver operating characteristics (ROC) curve. The best-performing prediction rates achieved by AHP and FR were 83% and 81%, respectively. Finally, the obtained results indicated that the AHP model achieved better performance than the FR model.

Groundwater potential mapping using multi-criteria decision, bivariate statistic and machine learning algorithms: evidence from Chota Nagpur Plateau, India

Increased consumption of water resource due to rapid growth of population has certainly reduced the groundwater storage beneath the earth which leads certain challenges to human being in recent time. For optimal management of this vital resource, exploration of groundwater potential zone (GWPZ) has become essential. We have applied Analytical Hierarchy Process (AHP), Frequency Ratio (FR) and two machine learning techniques specifically Random Forest (RF) and Naïve Bayes (NB) here to delineate GWPZ in Gandheswari River Basin in Chota Nagpur Plateau, India. To achieve the goal of the study, twelve factors that determine occurrence of groundwater have been selected for inter-thematic correlations and overlaid with location of wells. These factors include elevation, drainage density, slope, lithology, geomorphology, topographical wetness index (TWI), distance from the river, rainfall, lineament density, Normalized Difference Vegetation Index (NDVI), soil, and Land use and Land cover (LULC). A total 170 points including 85 in well site and 85 in non-well site have been selected randomly and allocated into two parts: training and testing at the share of 70:30. The implemented methods have significantly provided five GWPZs specifically Very Good (VG), Good (G), Moderate (M), Poor (P) and Very Poor (VP) with high and acceptable accuracy. The study also finds that geomorphology, slope, rainfall and elevation have greater importance in shaping GWPZs than LULC, NDVI, etc. Model performance has been tested with receiver operator characteristics (ROC), Accuracy (ACC), Kappa Coefficient, MAE, RMSE, etc., methods. Area under curve (AUC) in ROC curve has revealed that accuracy level of AHP, FR, RF and NB is 78.8%, 81%, 85.3% and 85.5, respectively. The machine learning techniques coupled with AHP and FR unveil effective delineation of groundwater potential area in said river basin which by genetically offers low primary porosity due to lithological constrains. Therefore, the study can be helpful in watershed management and identifying appropriate location wells in future.

Groundwater recharge estimation and potential recharge mapping in the Afram Plains of Ghana using SWAT and remote sensing techniques

The Afram Plains watershed is one of the major watersheds located at the lower part of the Volta Basin in Ghana with high groundwater usage. Dependency on groundwater is a fast-growing concern in the plains due to the unreliable supply of surface water. This has put the groundwater resources under intense pressure and has necessitated the need for sustainable management of the groundwater resources. This study aimed to estimate the recharge, spatial distribution, and potential recharge zones of groundwater at different scales in the Afram Plains area using the Soil and Water Assessment Tool (SWAT) model and Geography Information System (GIS) approach. The SWAT model was set up (1983–2016), calibrated (1983–1990) and validated (1991–1997) using digital elevation model, soil, land use, climate datasets, and discharge data. The GIS approach was used to produce thematic maps for eight contributing factors; rainfall, elevation, land cover land use, lineaments, drainage, geology, soil and slope for mapping the recharge zones. The results showed that about 1.65% (19.3 mm) of the SWAT simulated mean annual rainfall (1176.9 mm) is converted into groundwater recharge in the watershed. The potential recharge zones categorized as very poor, poor, moderate and high zones have area coverages of 0.58% (20.8 km2), 22.4% (798 km2), 60.9% (2169 km2), and 16.2% (576 km2), respectively. The potential recharge zones after validation over boreholes and wells data indicated that the Afram Plains area is dominated by the moderate potential zone.

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.

Integration of GIS-based Multi-criteria Analysis Techniques for the Delineation of Groundwater Potential Zones in Oyo state, Nigeria using Bayes’ Approach

Groundwater is the world’s most extracted raw material due to its incessant need for human consumption. This study was carried out to delineate groundwater potential zones in Oyo state, Nigeria using the integration of two GIS-based multi-criteria analysis techniques – Multi influencing factor (MIF) and Analytic hierarchy process (AHP). The Bayes’ integration approach for the recalculation of criteria weights was used. Eight groundwater potential contributing factors such as land cover, drainage density, lineament density, soil texture, geology, geomorphology, slope, and rainfall were processed and the multi-criteria analysis techniques were employed in assigning weights to each thematic layer and sub-classes. The thematic layers were overlaid in ArcGIS 10.4 software environment using the groundwater potential index equation for the generation of groundwater potential maps. The criteria weights of the MIF and AHP techniques were further integrated using Bayes’ approach to obtain an optimum groundwater potential map. In this study, the groundwater potential maps from the three techniques were validated using the Receiver Operating Characteristics (ROC) curve methods. The validation of the groundwater potential zonation maps from the MIF, AHP and the Bayes’ integration was also executed by evaluating the depths and yields from 1425 boreholes distributed across the study area. The Bayes’ approach shows that the groundwater percentage distributions within the study area are: very low (36%), low (34%), Moderate (14%) and high (16%). The maximum yields of 200m3 were observed in Akinyele, Atisbo and Egbeda LGA with minimum borehole depths of 24.20m, 30.30m and 30.00m. The Area under the Curve (AUC) results are: MIF (69.4%), Bayes’ (69.0%) and AHP (67.6%) respectively. The Bayes’ integration approach further shows better consistency as the average borehole yields across the groundwater potential zones positively correlates i.e. high potential zone has the highest average borehole yield, followed by the moderate, low and very low.

Delineating the groundwater potential zones in Bangladesh

Abstract The objective of this research is to identify groundwater potential zones for Bangladesh. Fourteen influential factors associated with topography, geology and meteorological concerns were applied for this study. Weights for factors and sub-factors within a factor were calculated based on pairwise comparisons. The groundwater potential zones were delineated through GIS-based weighted overlays of factor maps. According to the pairwise comparison, the consistency ratios for factors and sub-factors were within the allowable range (i.e., less than 0.10). General soil type (eigenvalue 0.17), geology (eigenvalue 0.16), and geomorphology (eigenvalue 0.15) were the most important factors in determining groundwater potential zoning. The groundwater potential index has maximum and minimum values of 45.99 and 10.34, respectively. According to the groundwater potential map, relatively higher groundwater potential zones were found in the southern parts of Bangladesh and along major rivers. The study's findings will be useful to government authorities in making evidence-based decisions about national water policy and planning.