Groundwater Prospect Zones Research Articles

A new insight for geophysical and geospatial mapping quaternary alluvium of Indo-Gangetic plains for exploration and assessment of groundwater in Haryana, India

A comprehensive study on groundwater exploration and assessment was conducted, integrating geophysical, geological, lithological, and resistivity logging techniques for a holistic evaluation of groundwater resources in Haryana, India. The primary research objective was to gain insight into the geological composition of alluvial sand and its implications for groundwater occurrence in Haryana's semi-arid region. The study showcases high-resolution electrical resistivity tomography results alongside conceptual cross-sectional models and 3D lithological models, revealing buried channels that serve as groundwater sources and saturated sand as well as recharge conduits within the alluvial region of Haryana. Moreover, borehole drilling at two sites and resistivity logging data demonstrate a strong correlation between various lithologies and resistivity logs. Overall, groundwater prospect zones are delineated at depths ranging from 18 m to over 100 m based on resistivity values of aquifer zones inferred from electrical resistivity tomography models, as well as mapped thick sand layers with significant hydrogeological importance in the study area. This emphasizes the stress on the aquifer system, where groundwater is being extracted from deeper depths. The conceptual litho-sectional model reveals a thick sand signature at intermediate depths, indicating the presence of buried paleochannels extending from Galedwa (Kurukshetra) to Jubbal (Yamunanagar). Additionally, the 3D lithological models, along with the electrical resistivity tomography results, facilitate strategic planning for shallow and deep boreholes for groundwater exploration and prospecting, aiming at sustainable groundwater development and management of resources in both the present and future.

Geomatics Application for Assessment of Groundwater Prospective Zones – A case study of Kanchi River Sub Basin, Khunti- Ranchi districts, Jharkhand.

Geomatics Application for Assessment of Groundwater Prospective Zones – A case study of Kanchi River Sub Basin, Khunti- Ranchi districts, Jharkhand.

Empowered machine learning algorithm to identify sustainable groundwater potential zone map in Jashore District, Bangladesh

Groundwater is a crucial natural resource that supplies potable water to the local population in Jashore, Bangladesh. In recent years, the district has experienced groundwater scarcity during the summer season, significantly affecting both domestic and agricultural usage, thereby impacting the local populace. However, accurately assessing its availability and quality remains a challenge. Our objective was to identify groundwater prospect zones in Jashore and provide valuable insights for management and decision-making through Explainable Artificial Intelligence (XAI). This study used 15 groundwater conditioning factors for potential zone mapping. The k-Nearest Neighbors (KNN), Neural Networks (NNET), Gradient Boosting Machines (GBM), Random Forest (RF), and Support Vector Machines (SVM) models were applied to assess groundwater suitability, resulting in the identification of five distinct areas with different levels of potential. The areas with very low suitability covered 39.37, 14.95, 107.23, 291.59, and 300.24 km2, while areas with very high suitability covered 1255.61, 969.49, 1221.99, 1958.85, and 1211.03 km2, respectively. Notably, the RF and NNET models exhibited higher accuracy, with Area Under Curve (AUC) values of 90%. The KNN, GBM, and SVM models achieved AUC values of 85%, 86%, and 88% respectively. The study reveals that the northeastern region has the greatest groundwater potential, while the southern and southwestern regions have lower capacities. The researcher's also utilized Partial Dependence Plots (PDP) technique, to analyze parameter behavior in predicting groundwater suitability. This approach provided valuable insights into the impact of various factors on groundwater potential, aiding management decisions. The study findings inform to policymakers and water resource managers in Jashore, enabling sustainable groundwater management by identifying high-potential areas. This knowledge optimizes resource allocation, ensuring sufficient supply for sustainable development.

Delineation of groundwater prospective zones using multivariate and spatial analysis techniques in Henan Province North China Plain

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

Mapping Groundwater Prospective Zones Using Remote Sensing and Geographical Information System Techniques in Wadi Fatima, Western Saudi Arabia

Integration of remote sensing (RS) and GIS methods has allowed for the identification of potential water resource zones. Here, climatic, ecological, hydrologic, and topographic data have been integrated with microwave and multispectral data. Sentinel-2, SRTM, and TRMM data were developed to characterize the climatic, hydrologic, and topographic landscapes of Wadi Fatima, a portion of western Saudi Arabia that drains to the Red Sea. The physical characteristics of Wadi Fatima’s catchment area that are essential for mapping groundwater potential zones were derived from topographic data, rainfall zones, lineaments, and soil maps through RS data and GIS techniques. Twelve thematic factors were merged with a GIS-based knowledge-driven approach after providing a weight for every factor. Processing of recent Sentinel-2 data acquired on 4 August 2023 verified the existence of a zone of vegetation belonging to promising areas of groundwater potential zones (GPZs). The output map is categorized into six zones: excellent (10.98%), very high (21.98%), high (24.99%), moderate (21.44%), low (14.70%), and very low (5.91%). SAR CCD derived from Sentinel-1 from 2022 to 2023 showed that the parts of no unity are in high-activity areas in agricultural and anthropogenic activities. The model predictions were proven with the ROC curves with ground data, existing wells’ locations, and the water-bearing formations’ thickness inferred from geophysical data. Their performance was accepted (AUC: 0.73). The outcomes of the applied methodologies were excellent and important for exploring, planning, managing, and sustainable development of resources of water in desert areas. The present study successfully provided insights into the watershed’s hydrologic, climatic, vegetated variation, and terrain database information using radar, optical, and multi-temporal InSAR data. Furthermore, the applied multi-criteria overlay technique revealed promising areas for groundwater abstraction, which can be applied elsewhere in various environmental situations.

Identifying groundwater prospect zones through earth observation techniques in Bilate watershed, Rift Valley Lakes Basin, Ethiopia

ABSTRACT Groundwater is one of the most valuable natural resources gifted to mankind, especially in surface water-scarce locations. Most parts of Ethiopia dependent on the amenity for common needs, irrigation, etc. The lack of a scientific approach to its exploitation and management makes the resource underutilized. Therefore, Remote sensing (RS) and Geographic information systems (GIS) were utilized to decipher potential groundwater zones (PGWZ) existing in the Bilate watershed of the nation to economize, speed up and technicalize the system. The ground truth of the results obtained was verified to confirm the reliability of the techniques used. Through RS, optical and microwave data were obtained, while GIS was helpful in collecting information on water points mapping and 2D resistivity survey in locating the aquifers. In particular, resistivity studies revealed both pyroclastic and rhyolite intrusions that act as water barriers and the basaltic and ignimbrite rocks that favour aquifer affluence. During the process of identification and quantification of PGWZ in the watershed, the analytical hierarchy process (AHP), weighted linear combination (WLC), and digital elevation model (DEM) were employed in thematic layer formation, reclassification, overlay analysis and map production. As a result, five PGWZ having very high (19.8%), high (35.3%), moderate (22.6%), low (19.3%), and very low (2.9%) prospects were identified. Similarly, over 75% of the area was moderate to high category in PGWZ. The accuracy of the qualitative groundwater inventory was 92.64 per cent. Thus, combining high-resolution optical/microwave data and GPS information with the AHP model has ascertained the accuracy of the PGWZ identified. So ultimately, the present work lays the foundation for the use of advanced spatial techniques in finding natural resources useful for the welfare of the nation and its populace.

Delineation of Ground Water Prospect Zones of Mojo Watershed, Ethiopia, East Africa, Using GIS, Remote Sensing and Analytical Hierarchy Process

Delineation of Ground Water Prospect Zones of Mojo Watershed, Ethiopia, East Africa, Using GIS, Remote Sensing and Analytical Hierarchy Process

Assessment of potential groundwater Zones in the drought-prone Harawa catchment, Somali region, eastern Ethiopia using geospatial and AHP techniques

Groundwater is essential for the sustainable growth of society, economy, and agriculture. Identifying potential zones for groundwater is required to fulfill the needs of various sectors. This study aims to identify prospective groundwater zones in the drought-prone Harawa catchment area of the Somali region of eastern Ethiopia using geospatial and analytical hierarchy process (AHP) approaches. Eight significant groundwater regulating variables were developed by combining primary and remote sensing data, and AHP analysis was performed to identify groundwater prospect zones. The findings of the prospective groundwater zone model were categorized into four groups: very low, low, moderate, and high. Approximately 14.2% of the research area was recognized as having high potential, 24.0% as having moderate potential, and 38.7% and 23.2% as having very low and low potentials, respectively. Groundwater prospect maps were examined and confirmed using the discharge data from numerous production wells in the study region. The results demonstrated a strong correlation or agreement with the yield data. The curve approach was cross-validated with the area, revealing an 81.7% correlation between the model findings and the water well points. The sensitivity analysis showed that lithology, land use/land cover, and rainfall significantly impacted groundwater occurrence. In contrast, the slope of the study region had little effect.

Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. Remote sensing data can predict the location of potential water resources. The current study was conducted in China’s Yellow River region, Ningxia Hui Autonomous Region (NHAR). Through the use of a GIS-based frequency ratio machine learning technique, nine layers of evidence influenced by remote sensing data were generated and integrated. The layers used are soil characteristics, aspect, and roughness index of the terrain, drainage density, elevation, lineament density, depressions, rainfall, and distance to the river from the location. Six groundwater prospective zones (GWPZs) were found to have very low (13%), low (30%), moderate (25%), high (16%), very high (11%), and extreme potentiality (5.26%) values. According to well data used to validate the GWPZs map, approximately 40% of the wells are consistent to very high to excellent zones. Information about groundwater productivity was gathered from 150 well locations. Using well data that had not been used for model training, the resulting GWPZs maps were validated using area-under-the-curve (AUC) analysis. FR models have an accuracy rating of 0.759. Landsat data were used to characterize the study area’s changes in land cover. The spatiotemporal differences in land cover are detected and quantified using multi-temporal images which revealed changes in water, agricultural, and anthropogenic activities. Overall, combining different data sets through a GIS can reveal the promising areas of water resources that aid planners and managers.

Mapping Potential Water Resource Areas Using GIS-Based Frequency Ratio and Evidential Belief Function

Groundwater is a critical freshwater resource that is necessary for sustaining life. Thus, targeting prospective groundwater zones is crucial for the extraction, use, and management of water resources. In this study, we combined the remote sensing, GIS-based frequency ratio (FR), and evidential belief function (EBF) techniques into a model to delineate and quantify prospective groundwater zones. To accomplish this, we processed Shuttle Radar Topography Mission (SRTM), Landsat-8 Operational Land Imager (OLI), Sentinel-2, and rainfall data to reveal the geomorphic, hydrologic, and structural elements and climatic conditions of the study area, which is downstream of the Yellow River basin, China. We processed, quantified, and combined twelve factors (the elevation, slope, aspect, drainage density, lineament density, distance to rivers, NDVI, TWI, SPI, TRI, land use/cover, and rainfall intensity) that control the groundwater infiltration and occurrence using the GIS-based FR and EBF models to produce groundwater potential zones (GWPZs). We used the natural breaks classifier to categorize the groundwater likelihood at each location as very low, low, moderate, high, or very high. The FR model exhibited a better performance than the EBF model, as evidenced by the area under the curve (AUC) assessment of the groundwater potential predictions (FR AUCs of 0.707 and 0.734, and EBF AUCs of 0.665 and 0.690). Combining the FR and EBF models into the FR–EBF model increased the accuracy (AUC = 0.716 and 0.747), and it increased the areas of very high and moderate potentiality to 1.97% of the entire area, instead of the 0.39 and 0.78% of the FR and EBF models, respectively. The integration of remote sensing and GIS-data-driven techniques is crucial for the mapping of groundwater prospective zones.

Delineating Groundwater Potential Zones in Hyper-Arid Regions Using the Applications of Remote Sensing and GIS Modeling in the Eastern Desert, Egypt

The increasing demand for freshwater supplies and the effects of climate change in arid and hyper-arid regions are pushing governments to explore new water resources for food security assurance. Groundwater is one of the most valuable water resources in these regions, which are facing water scarcity due to climatic conditions and limited rainfall. In this manuscript, we provide an integrated approach of remote sensing, geographic information systems, and analytical hierarchical process (AHP) to identify the groundwater potential zone in the central Eastern Desert, Egypt. A knowledge-driven GIS-technique-based method for distinguishing groundwater potential zones used multi-criteria decision analysis and AHP. Ten factors influencing groundwater were considered in this study, including elevation, slope steepness, rainfall, drainage density, lineament density, the distance from major fractures, land use/land cover, lithology, soil type, and the distance from the channel network. Three classes of groundwater prospective zones were identified, namely good potential (3.5%), moderate potential (7.8%), and poor potential (88.6%) zones. Well data from the study area were used to cross-validate the results with 82.5% accuracy. During the last 8 years, the static water level of the Quaternary alluvium aquifer greatly decreased (14 m) due to excessive over pumping in the El-Dir area, with no recorded recharges reaching this site. Since 1997, there has been a noticeable decline in major rainfall storms as a result of climate change. The current study introduces a cost-effective multidisciplinary approach to exploring groundwater resources, especially in arid environments. Moreover, a significant modern recharge for shallow groundwater aquifers is taking place, even in hyper-arid conditions.

An integrated approach for the application of geographical information system and Remote Sensing for prospective groundwater zones in Surat region, India

Sustainable groundwater management is becoming a priority in the context of significant changes in groundwater use patterns, particularly with continuously increasing demand for groundwater due to growing population, expansion of irrigation area, and economic progress. This research contributed to the integration of GIS-RS and a fuzzy technique to delineate groundwater prospective zones for Surat district, India. In this study, nine different fuzzy membership layers of influencing parameters were overlaid using the fuzzy GAMMA operator to generate the prospective zones. The result indicates that 62.53% area of the district falls under the high prospective zone. Sensitivity analysis reveals that lineament density, rainfall distribution, and geomorphology were the most important parameters influencing the region's groundwater potentiality. The RS-GIS-based Fuzzy method is proficient and efficient and can be applied anywhere on the earth to identify and delineate groundwater prospective zones.

Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions

Remote sensing and GIS approaches have provided valuable information on modeling water resources, particularly in arid regions. The Sahara of North Africa, which is one of the driest regions on Earth, experienced several pluvial conditions in the past that could have stored significant amounts of groundwater. Thus, harvesting the stored water by revealing the groundwater prospective zones (GWPZs) is highly important to water security and the management of water resources which are necessary for sustainable development in such regions. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Tropical Rainfall Measuring Mission (TRMM), and Landsat-8 OLI data have all successfully revealed the geologic, geomorphic, climatic, and hydrologic features of Wadi El-Tarfa east of Egypt’s Nile River. The fusion of eleven predictive GIS maps including lithology, radar intensity, lineament density, altitude, slope, depressions, curvature, topographic wetness index (TWI), drainage density, runoff, and rainfall data, after being ranked and normalized through the GIS-based analytic hierarchy process (AHP) and weighted overlay methods, allowed the GWPZs to be demarcated. The resulting GWPZs map was divided into five classes: very high, high, moderate, low, and very low potentiality, which cover about 10.32, 24.98, 30.47, 24.02, and 10.20% of the entire basin area, respectively. Landsat-8 and its derived NDVI that was acquired on 15 March 2014, after the storm of 8–9 March 2014, along with existing well locations validated the GWPZs map. The overall results showed that an integrated approach of multi-criteria through a GIS-based AHP has the capability of modeling groundwater resources in arid regions. Additionally, probing areas of GWPZs is helpful to planners and decision-makers dealing with the development of arid regions.

Near-surface groundwater exploration using the geoelectrical resistivity method: A case study of Wadi Nisah, Riyadh, Saudi Arabia

In desert regions like Saudi Arabia, groundwater exploration is critical for drinking, household, and agricultural purposes. The purpose of this study is to use Vertical Electric Sounding (VES) to assign near-surface groundwater sources in the Wadi Nisah area of central Saudi Arabia. Twenty VES stations were installed in the central part of Wadi Nisah, with a maximum half current electrode separation (AB/2) of 100 m, utilizing a Syscal R2 resistivity-meter with Schlumberger electrode design. The interpretation of these VES's revealed five geoelectric layers, the first of which is formed of dry sand with greater resistivity values and has a depth of around 3 m. The groundwater-bearing aquifer is represented by the fifth layer, which is the deepest, with depths ranging from 37.8 to 52.25 m below the surface and resistivities ranging from 47 Ω-m to 115.3 Ω-m. Six geoelectrical cross-sections were also mapped to show the lateral and vertical heterogeneities of the underlying lithology in the study area. These findings suggest that the groundwater bearing layer is a hydrologically promising zone capable of delivering optimal groundwater yield for drinking and agricultural use in the study area; the VES technique is a useful tool for identifying groundwater prospective zones.

Multi-sensor satellite images and resistivity survey for the evolution of Sutlej Palaeochannels and groundwater aquifer potential in Punjab, Northwest India

Remote Sensing technique with multi-sensor satellite images is found to be a useful tool in delineation of the palaeochannel network in arid to semi-arid regions in India. Principal Component Analysis method with fused images (optical and microwave data) shows a wide N-S trending Sutlej Palaeochannels (SPC) and narrow NE-SW trending palaeochannels in central Punjab. High-resolution Electrical Resistivity Tomography (HERT) survey and litholog data analysis provide good groundwater prospect zone of the palaeochannel. Interpretation of 2D resistivity tomograms with Wenner-Schlumberger method exhibit a thick zone of low resistivity layer (saturated coarse-grained sand) at a depth of 40 m. Integrated geo-tectonic study provides clue that the river migration of Sutlej River from N-S to E-W direction was due to a major tectonic upliftment in the Himalaya. Geochronological data analysis indicates that the avulsed channel of SPC was the main course of old Ghaggar River, linked to the ‘Lost Saraswati River’ in northwest India.

GIS-based multi-criteria approach to delineate groundwater prospect zone and its sensitivity analysis

This study was aimed at delineating groundwater potential zones (GWPZ) using geo-spatial techniques for Ranchi district, Jharkhand (India). Data including Cartosat-1 digital elevation model (DEM), Landsat 8 satellite images, lithology, geology, soil, and water yield data were utilised in this study. The relative importance of multiple parameters including lithology, soil, slope, geology, rainfall, drainage density, and land use/land cover (LULC) that influence the availability of groundwater was determined subjectively. Analytical hierarchy process (AHP) along with pair-wise comparison decision theory was utilized to calculate the weights for each aforementioned parameter. The delineated GWPZ were categorized into four classes viz., very good zone (31.57%), good zone (45.43%), moderate zone (13.09%), and poor zone (8.53%). The sensitivity analysis indicated lithology and soil type as the most and least sensitive parameters, respectively influencing the presence of groundwater in the study area. Comparison between well discharge data and delineated GWPZ yielded a coefficient of determination (R2) of 0.59. This study contributes to identifying priority areas where appropriate water conservation programs as well as strategies for sustainable groundwater development can be implemented.

Integrating GIS and remote sensing for delineation of groundwater potential zones in Bundelkhand Region, India

The present research aims to find the groundwater prospective areas by using Geographic Information System and Remote Sensing based frequency ratio technique. The data sets that affect groundwater such as geomorphology, lithology, slope, soil, rainfall, drainage density, lineament density and land use/ land cover were taken under consideration for finding the groundwater prospective areas in the Bundelkhand region, India. Thematic maps of all the above-mentioned factors have been prepared from the remotely sensed data, data taken from the different organizations and departments. The weights of each attribute class of each thematic layer are assigned according to the frequency ratio technique. A total, 799 numbers of spring locations and discharge rate data of 108 numbers of well locations have been taken under consideration for obtaining the frequency ratio of each attribute class of every parameter used and testing the obtained result, respectively. Finally, all layers were combined by raster calculator command in ArcGIS software to develop the map of groundwater prospective zones in the Bundelkhand region. The produced groundwater prospective areas were classified into five different zones such as very high, high, moderate, low and very low prospective zones and verified by actual well yield data.

Multi-criteria decision making and Dempster-Shafer model-based delineation of groundwater prospect zones from a semi-arid environment.

The present study illustrates the delineation of the groundwater potential zones in one of the most critical and drought-affected areas under Bundelkhand region of Uttar Pradesh (India). Hydrological evaluations were carried out using GIS tools and remote sensing data which ultimately yielded several thematic maps, such as lineament density, land use/land cover, drainage density, lithology, slope, geomorphology, topographic wetness index (TWI), DEM, and soil. Thematic layers were assigned relative weightages as per their groundwater potential prospects under multi-criteria decision making (MCDM) method through analytical hierarchy process (AHP). To recognize the groundwater potential zone, weighted overlay analysis was also performed. Additionally, for testing of the Dempster-Shafer model, 16 wells in the study area have been selected. Based on the probability of the groundwater occurrence, the belief factor was equated to delineate groundwater potential zones which illustrate five different potential zones. According to the AHP model, the northwest side of the study area is characterized with very high potential zones whereas the northeast and southeast regions constitute medium and low groundwater potential zones respectively. According to the DS model, very high groundwater potential zones constitute 17% and the remaining area falls under low potential. Overall accuracy of the DS model is higher than AHP model.

Integration of shannon entropy (SE), frequency ratio (FR) and analytical hierarchy process (AHP) in GIS for suitable groundwater potential zones targeting in the Yoyo river basin, Méiganga area, Adamawa Cameroon

Study regionYoyo river basin, Méiganga, Adamawa region, Cameroon. Study focusThe study aimed firstly to delineate and classify groundwater prospective zones in granito-gneissic terrain using advanced applications of remote sensing and geographical information systems through the integration of analytical hierarchy process (AHP), frequency ratio (FR) and Shannon entropy (SE). Secondly to compare the performance of these models to ensure effectiveness in computation. Thirteen groundwater influencing factors were adopted and weights were assigned to each thematic layer based on their relationship and characteristic with groundwater occurrence. Overlay weighted linear combination analysis tool in Arcgis environment was applied for groundwater prospect maps constructed. New hydrological insights for the regionGroundwater maps was classified into four categories:I poor, moderate, good and excellent. These maps analysis revealed that AHP have performed well for good and excellent classes with percentage of coved area of 23.73% and 23.75% respectively. These areas are localised in central, south-eastern, north-western and southern parts of the basin. Validation of outputs maps using wells location showed that AHP have higher accuracy (Kappa= 79.53%, AUC (predicted) = 82.40%, AUC (success) = 87.09%), compared to FR (Kappa= 74.62%, AUC (predicted) = 76.68%, AUC (success) = 77.21%) and to SE (Kappa= 77.75%, AUC (predicted) = 70.61.40%, AUC (success) = 75.78%). These results are reliable and useful for various decision-making processes and sustainable management of groundwater resources in the Yoyo river basin.

Electrical Resistivity Sounding for Groundwater Investigation around Enugu Metropolis and the Environs, Southeast Nigeria

This report evaluates the use of electrical method and borehole data to investigate the subsurface to delineate the groundwater potential in Enugu metropolis and the environs, south-eastern Nigeria other than rely only on resistivity method which could lead to interpretation error. Integrating these 2 data sets is key in this study. The study area is located in the Anambra Basin and is underlain by Nkporo/Enugu Shale which is overlain by the Mamu Formation. It is bounded by Latitudes 6°2 0'00"N to 6°30'00"N and Longitudes 7°25'00"E to 7°35'00"E and covers surface area of about 342 m2. Thirty-one vertical electrical soundings (VES) were carried out across the area using the Schlumberger electrode array with current electrode separation from 2 to 500 m to identify the depths and resistivity values of the identified geo-electric layers. Through data analysis using WinResist software, the apparent resistivity, thicknesses and depths and the thicknesses of the aquifers were generated. The resistivity and depths were modelled to generate resistivity map and depth map. The resistivity of the aquiferous zone within the study area varied from 20.55 - 427.8 ohm-m at depths of between 10.7 - 40.05 m. Depth to the water table appears to be shallow at the south western part of the map. The interpreted geo-electric layers show a sequence of lateritic top soil, shale, sand and shale. The frequency distribution of the VES curves generated shows the presence of 3 to 5 layers with HK type as the highest. Also, a 2D model was generated using the correlation of VES to VES data and borehole data to VES data to show the underlying stratigraphy beneath the study area as well as the direction of ground water flow. Result of the VES curve analysis reveals that the sub-surface is underlain by three lithological layers namely: lateritic top soil, shale, sand and shales with NW direction of groundwater flow from the 2D model. Groundwater prospective zones can be seen along NW, SW and central parts of the study area which have low resistivity values.