Groundwater Potential Areas Research Articles

Groundwater potential assessment using GIS-based ensemble learning models in Guanzhong Basin, China

Groundwater plays a crucial role in sustaining industrial and agricultural production and meeting the water demands of the growing population in the semi-arid Guanzhong Basin of China. The objective of this study was to evaluate the groundwater potential of the region through the use of GIS-based ensemble learning models. Fourteen factors, including landform, slope, slope aspect, curvature, precipitation, evapotranspiration, distance to fault, distance to river, road density, topographic wetness index, soil type, lithology, land cover, and normalized difference vegetation index, were considered. Three ensemble learning models, namely random forest (RF), extreme gradient boosting (XGB), and local cascade ensemble (LCE), were trained and cross-validated using 205 sets of samples. The models were then applied to predict groundwater potential in the region. The XGB model was found to be the best, with an area under the curve (AUC) value of 0.874, followed by the RF model with an AUC of 0.859, and the LCE model with an AUC of 0.810. The XGB and LCE models were more effective than the RF model in discriminating between areas of high and low groundwater potential. This is because most of the RF model's prediction outcomes were concentrated in moderate groundwater potential areas, indicating that RF is less decisive when it comes to binary classification. In areas predicted to have very high and high groundwater potential, the proportions of samples with abundant groundwater were 33.6%, 69.31%, and 52.45% for RF, XGB, and LCE, respectively. In contrast, in areas predicted to have very low and low groundwater potential, the proportions of samples without groundwater were 57.14%, 66.67%, and 74.29% for RF, XGB, and LCE, respectively. The XGB model required the least amount of computational resources and achieved the highest accuracy, making it the most practical option for predicting groundwater potential. The results can be useful for policymakers and water resource managers in promoting the sustainable use of groundwater in the Guanzhong Basin and other similar regions.

Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco

An assessment of potential groundwater areas in the Ifni basin, located in the western Anti-Atlas range of Morocco, was conducted based on a multicriteria analytical approach that integrated a set of geomorphological and hydroclimatic factors influencing the availability of this resource. This approach involved the use of geographic information systems (GIS) and hierarchical analytical process (AHP) models. Different factors were classified and weighted according to their contribution to and impact on groundwater reserves. Their normalized weights were evaluated using a pairwise comparison matrix. Four classes of potentiality emerged: very high, high, moderate, and low, occupying 15.22%, 20.17%, 30.96%, and 33.65%, respectively, of the basin’s area. A groundwater potential map (GWPA) was validated by comparison with data from 134 existing water points using a receiver operating characteristic (ROC) curve. The AUC was calculated at 80%, indicating the good predictive accuracy of the AHP method. These results will enable water operators to select favorable sites with a high groundwater potential.

Integrated Geospatial and Geophysical Approaches for Mapping Groundwater Potential in the Semi-Arid Bukombe District, Tanzania

The rapid growth of civil societies coupled with population influx due to the artisanal mining industry in the Bukombe district (BD) has triggered a high demand for water resources. The daily consumption of water resources in the district surpasses the supply from available surface water sources. Thus, the situation has raised the demand for groundwater resources as an alternative. Despite the importance of groundwater resources, no current studies have spatially assessed groundwater potential to locate optimal points for borehole development. This study intended to investigate and map the groundwater potential areas (GWPAs) in the semi-arid BD using remote sensing (RS), the geographic information system (GIS), and the analytic hierarchy process (AHP) to help local communities access clean and safe water. Rainfall, geology, slope, drainage density, land use/land cover and lineament density were prepared to delineate the map of GWPAs. The map was categorized into poor (0.21%), moderate good (51.39%), good (45.70%) and very good (2.70%). Finally, the GWPA map was validated using Vertical Electrical Sounding (VES), 2-D sections and a drilled borehole. The validation results confirmed that the applied approach provides significant results that can help in planning the sustainable utilization of groundwater resources.

Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India

Mapping the groundwater potential zones with high accuracy is always a difficult task. The combination of Geographic Information System (GIS) and Remote Sensing (RS) with machine learning techniques provide a reliable method to map the groundwater prospective areas. This research used support vector machine (SVM) learning and random forest (RF) regression algorithms to predict the groundwater potential areas in the Bundelkhand craton region. All the parameters that affect the groundwater occurrence in this region, such as altitude, aspect, distance to drainage, distance to road, distance to faults, geomorphology, distance to river, lithology, normalized difference vegetation index (NDVI), rainfall, slope, soil, drainage density, land use land cover (LULC), topographic wetness index (TWI), lineament density and curvatures have been prepared by the remote sensing data as well as data taken from different departments and organizations. The training and testing dataset is generated from the groundwater potential zones map prepared through the frequency ratio (FR) technique. A total of 23,917-pixel locations have been selected in the research region. These locations contain the location of groundwater points and non-groundwater points equally. These points were randomly portioned into 70:30 for training and testing the model, respectively. The 2417 unknown points were taken from the study area and given to the trained model to predict the groundwater prospective areas. The maps of groundwater potential zones obtained using machine learning models were categorized into five classes: very low, low, moderate, high, and very high. The outcome of the employed algorithm is validated through the well discharge data and area under the receiver operating characteristics curve (AUC-ROC) method. The developed model's outcome gives valuable information regarding the effective management of groundwater in a particular region to government agencies and private sectors.

Probabilistic Determination of Groundwater Using Semi-Qualitative MCDA-Based Analytic Hierarchy Process Approach in Sunsari District, Nepal

Groundwater served as the major source of water for existing biophysical species in an ecosystem. In recent years, depletion of the level of groundwater becomes an emerging serious environmental issue due to the anti-reciprocal man and human activities such as global climate change. This study aims to examine the groundwater potential areas using the semi-qualitative research design, based on Analytic Hierarchy Process. The focused is based on the Geographic Information System for the analysis of determining factors, such as distance from the stream and river, precipitation, pond frequency, normalized difference water index, land-use/land cover, drainage density, slope gradient, soil, and topographic wetness index. The Receiver Operating Characteristics were used to check the accuracy of the final calculated map of the groundwater potential zone. The 5 points Likert scale ranges very poor (1) to very high (5) used to analyse the groundwater potential zones. The result shows that 0.81 percent area is very poor for the potentiality of groundwater in the study area. Other scale contained 8.13 for poor, moderate (19.94%), high (39.72%) and very high potential zones (31.41%). The Receiver Operating Characteristics result showed that under the curve success rate is 0.64% and the prediction rate is 0.76%. This result shows a reliable degree of predictability of groundwater near the spatial distribution of marshes, lakes, and water bodies in the study area. The finding shows that the very high potentiality of groundwater areas are determining by the factors of precipitation, pond frequency, distance from river and stream, drainage density, land-use/land cover, soil, and slope in the study area. The result of this empirical analysis can be applied to analyze sustainable and effective water resources management activities.

Groundwater potential assessment based on GIS-based Best-Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) Method.

In this study, the most suitable areas in terms of groundwater potential within the borders of the adjacent area of Sivas Municipality (Sivas/Turkey) were determined with the help of Geographic Information System (GIS)-based Best-Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) methods. Slope, drainage density, Topographic Position Index (TPI), lineament density, lithology, soil types, land use, geomorphology, and rainfall criteria were selected to determine groundwater potential areas. These criteria were weighted with the help of BWM, SWARA, and BWM-SWARA methods and the Groundwater Potential Index (GPI) was calculated according to the weighted linear combination method. According to the calculated GPI values, the groundwater potential of the study area was represented as "excellent," "very good," "good," "moderately good," "low," and "very low." According to all three methods, areas in the "excellent" class constituted 10.99%, 8.40%, and 11.16% of the study area, respectively, while areas in the "very low" class covered 8.33%, 7.98%, and 9.04% of the study area, respectively. The linear correlation coefficient (R2) values of the BWM, SWARA, and BWM-SWARA methods were calculated as 0.80, 0.82, and 0.75, respectively, while the area under the curve (AUC) values were determined as 0.83, 0.79, and 0.81, respectively. These results showed that the accuracy of the model was "very good" overall. As a result, groundwater potential mapping created for the study area will contribute to better development of groundwater resources and water management planning.

Integrated hydrogeophysical and GIS based demarcation of groundwater potential and vulnerability zones in a hard rock and sedimentary terrain of Southern India

This research has identified the groundwater potential and vulnerability zones in Tiruchirappalli district of Tamil Nadu, India. The Schlumberger electrode array has been used to conduct vertical electrical sounding (VES) at 95 sites with a maximum electrode spacing of 150 m. The study area comprises of hard rock and sedimentary formations. Geographical Information System (GIS) has been used to integrate the geoelectrical data and to prepare spatial variation maps for various parameters. Finally, groundwater potential and vulnerability zones have been demarcated, and these outputs have been validated using water level and nitrate data, respectively. The Dar-Zarrouk parameters such as longitudinal conductance (S), transverse unit resistance (T), and aquifer anisotropy (λ) have been used along with the spatial variation of resistivity and aquifer thickness to find out groundwater potential areas with the support of GIS. The thickness of topsoil, weathered zone and fractured zone are not uniform in the research area. Top soil plus weathered zone acts as a water table (phreatic) aquifer, which extends up to 38 m from the surface. Fractured zone extends up to 45 m, which acts as a kind of confined/semi-confined aquifer. Open and bore wells have been constructed to tap groundwater from the unconfined (water table) and confined/semi-confined aquifers, respectively. High to very high groundwater potential areas are associated with low resistivity, high thickness, low longitudinal conductance, high transverse unit resistance and high aquifer anisotropy areas. Very high groundwater potential areas are mostly confined to flood plain (alluvium) deposits in the central portion of the study area. High potential areas are noticed in the northern part, whereas low potential areas are noticed in the southern part. The areas with high longitudinal conductance indicate low permeable zones with less possibility of external pollution. Since agriculture is an important activity in the study region, this work will be useful to provide water supply for irrigation as well as for domestic needs.

Mapping the Groundwater Potential Areas by Using the GIS Method in Kg Samawang, Sandakan, Sabah, Malaysia.

This study aims to study the groundwater potential areas around Kg Samawang, Sandakan by using the Geographic Information System (GIS) method. There are few basic maps and data were used in this study, which is geology, drainage, soil type, lineament, topography, satellite images and rainfall distribution data. A total of seven thematic maps were produced including the lineament density map, drainage density map, landuse map, soil type map, rainfall distribution map, lithology map and topographic elevation map by using the ILWIS 3.0 software. All the thematic maps will be given a weightage based on its compensation to groundwater availability before being integrated based on the Hopkins Linear Equation using the Multi-Class Index Overlay model. This process will produce a forecast map of the groundwater potential at the study area. As a result of the forecast map, it is estimated that 40% of the study area has high potential of groundwater while 60% has medium, low and very low potential. It is hoped that this groundwater potential map can be used for the exploration work in the future.

Delineation of groundwater potential areas using RS/GIS and geophysical methods: a case study from the western part of Iran

Delineation of groundwater potential areas using RS/GIS and geophysical methods: a case study from the western part of Iran

Groundwater potential mapping using the integration of AHP method, GIS and remote sensing: a case study of the Tabelbala region, Algeria

Recently, groundwater resources are assessed and evaluated using Geographic Information System (GIS) and remote sensing technologies due to their effectiveness and wide spatial coverage. This work aims to identify groundwater potential areas in the Tabelbala region which lies in the Algerian desert in order to help for the solution of water resources shortages. GIS and remote sensing are employed in the preparation of the controlling factors such as lithology, lineaments, drainage network, slope, land use/land cover, topographic wetness index, and elevation. Statistical Analysis, as well as interpretation of remote sensing data, allow the extraction of important features about the study area and its characteristics. The prepared layers are combined with multicriteria analysis to identify the groundwater potential zones (GWPZs) based on their statistical weights. To validate the conducted work, 222 wells/boreholes are collected and prepared to assess the potential areas. Results reveal that the very good potentiality class covers approximately 8.81% of the total area while 6.47% shows very poor potentiality. In addition, the application of the ROC curve shows an AUC of 89% which reveals the effectiveness of the proposed approach. The final resulting map can be used for the identification of suitable sites for wells implantation.

Groundwater potential mapping based on long time series remote sensing data in Penghu Islands, China

AbstractIslands are special bodies of land surrounded by seawater. Many islands are facing water shortages as the demand for groundwater increases, thus, highlighting the importance and urgency of the assessment, planning, and management of island groundwater resources. To facilitate the same, the groundwater potential of the Penghu Islands in China was assessed for the first time using remote sensing (RS), geographic information system (GIS), and the analytic hierarchy process (AHP). Long‐term series remote sensing studies were also conducted to monitor the dynamic change in groundwater potential and to identify groundwater potential areas. In this study, the geological environment factors were normalized, and their weight was allocated based on the AHP of RS and GIS. The groundwater potential assessment (GPA) index, established using the weighted comprehensive algorithm, was used to analyse groundwater potential and demarcate groundwater potential zones. Finally, the trend in the spatio‐temporal variation of groundwater potential was analysed using long‐term series remote sensing data. The results from 2015 to 2021 showed that the Penghu Islands can be divided into five groundwater potential zones of different grades, such as 6.3%–8.1% of grade I, 22.2%–24.2% of grade II, 23%–24.4% of grade III, 19.6%–21% of grade IV, and 25.1%–25.5% of grade V. The results of the GPA were verified by comparing them with the abundance of water‐content rock formation in hydrogeologic maps, which yielded correlation coefficients of 78.3%–88.9%. Overall, the groundwater potential zones of different grades in the study area tended to be stable all year‐round, including the zones of grades I and II. The results of this study highlighted the reliability of predicting groundwater potential based on long‐term series remote sensing data and its usability to local personnel in appropriate groundwater resource planning and management.

Geospatial analysis for the identification and mapping of groundwater potential zones using RS and GIS at Eastern Gojjam, Ethiopia

Groundwater, an impressive source of quality water particularly for potable and irrigation. Geospatial analysis of identifying and mapping groundwater potential zones significantly reduce the costs and time of exploration and proper allocation of dried boreholes that helps to avoid failure of groundwater withdrawal planning and development. This research aimed to analysis geospatial identification and mapping of groundwater potential zones. In this study, Digital Elevation Model (DEM) data used to prepare a slope and drainage density map. Lineaments (automatically extracted with PCI Geomatica) and land use/land cover maps were derived from Sentinel 2A data. Supervised classification (maximum likelihood classifier) was applied with an overall accuracy of 84.34% and kappa coefficient of 0.825. Rainfall data from twenty-two meteorological stations was used to prepare rainfall thematic map using Inverse Distance Weight interpolation method. All factors were reclassified based on their class value into five classes, namely: ‘very high’, ‘high’, ‘moderate’, ‘low’, and ‘very low’. A Multi-Criteria Decision Analysis with Analytical Hierarchy Process was implemented with a consistency ratio (CR) value of 0.0098, which is < 0.1. Findings were obtained using a weighted sum overlay analysis of each factor's value multiplied by its corresponding weight. Therefore, 12.78% and 24.1% of the total area has ‘very high’ and ‘high’ potential for groundwater, respectively. Whereas, 29.2%, 24.26%, and 9.65% of the zone have ‘moderate’, ‘low’ and ‘very low’ potential, respectively. Finally, the result was validated with borehole data and the overall accuracy was found to be 87.17%. In conclusion, the application of RS and GIS are powerful tools to identify and map groundwater potential areas with a combination of different groundwater contributing factors.

Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin

Substantial mineral resources are enriched in the arid endorheic basins; however, due to environmental constraints, these areas face water shortages as well as its extremely uneven spatiotemporal distribution, which restricts the development of local industry and agriculture. Identifying these areas of the high groundwater potential are useful for groundwater supply and its sustainable planning. In this study, the Qaidam Basin in Northwest China was taken as an example. We collected 17 conditioning factors (i.e., precipitation, evaporation, geology, soil, Topographic Wetness Index, Fractional Vegetation Cover, distance to rivers, river density, distance to roads, road density, distance to faults, fault density, slope, curvature, residential density, landcover, and geomorphology) affecting groundwater resources in arid areas. We also collected 139 groundwater samples and used random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) (associated with one-hot encoding) to predict the groundwater potential in this area. The Qaidam Basin was discretized into 420,000 sample points calculated in turn by the above three models. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to test the accuracy of the three methods. Results indicated that the prediction scores for the three methods were 0.742, 0.790, and 0.817, and the AUC was 0.783, 0.811, and 0.846, respectively. The result provided by CNN was more precise than the results provided by RF and DNN. Additionally, this study aims to investigate the effects of the aforementioned factors on groundwater potential. A total of 17 factors were combined with the Geodetector model to quantify their impacts and interactions on the groundwater potential of the Qaidam Basin. Results revealed that the critical factors affecting groundwater potential in the Qaidam Basin were geomorphology (0.183) and evaporation (0.144), and their combined contribution was 0.457. The influence of arbitrary two-factors on groundwater potential is larger than that of themselves, demonstrating linear or nonlinear enhancement between them and confirming that the factor selections were sensible. The method based on CNN-Geodetector provides a novel approach for calculating groundwater potential, selecting appropriate evaluation indicators and quantifying the driving factors in the arid endorheic basins.

Hydrogeophysical exploration of groundwater potential in southwestern basement terrain of part of Ago-Iwoye, South western Nigeria.

Electrical resistivity survey of Ago-Iwoye southwestern Nigeria was done with the aim of delineating potential zones for groundwater exploitation. The rock types present are mainly gneissic rocks; granite gneiss, banded gneiss, biotite gneiss and porphyroblastic gneiss.Forty five (45) Vertical Electrical Soundings (VES) of schlumberger array were carried out using maximum electrode spacing (AB/2) of 100m. The results obtained were interpreted using partial curve matching which serves as model input for the computer iteration using WINRESIST software to determine the resistivity, thickness and depth of each layer in the subsurface. The identified curve types are H, KH, HKH, A, K and QH types while the H type curve is predominant. The three prominent geo-electric layers were inferred, these are the topsoil, weathered layer (sand/clay/clayey sand) and fresh/fractured bedrock with resistivity values of 33.4Ωm - 2266.3Ωm, 8.3Ωm - 1106.3Ωm and 68.4Ωm - 21729.6Ωm respectively. The isopach reveals overburden thickness range of 2m – 52m, bedrock resistivity ranges from 1000Ωm - 19000Ωm and reflection coefficient range from 0.62 -0.92.The study area is predominantly of low yield but areas in the northeastern and western part of the study area have high aquiferous groundwater potential areas are only noticeable in the northeastern and western part of the study area.

3D Quantitative Prediction of the Groundwater Potential Area-A Case Study of a Simple Geological Structure Aquifer.

The Ordos Basin is a sedimentary basin located in Inner Mongolia, China, where coal and uranium coexist. Water inrush disasters have always been one of the main disasters that threaten the safety of coal mine production, and thus, the study and division of groundwater potential regions are of great significance for the prevention of water inrush disasters and in situ leaching of sandstone-type uranium ore. A new method combining truncated Gaussian simulation and sedimentary facies control was established to predict the groundwater potential area. Taking a typical aquifer, the Zhiluo Formation, as an example, based on high-resolution sequence stratigraphy, geophysics, sedimentary geology, and geostatistical theory, the plane distribution of sand bodies was predicted. Furthermore, the relationship between rock porosity and electricity porosity was established to calculate the regional porosity. Combined with truncated Gaussian simulation and facies-controlled modeling methods, a facies-controlled heterogeneous property model was established to analyze the heterogeneous effective porosity of the aquifer in the study area. Groundwater potential areas were quantitatively evaluated by 3D modeling analysis. The results of the evaluated model were verified by actual data and provide a geological guarantee for the accurate mining of deep coal and uranium ore. A 3D distributed model of chemical elements, which is meaningful for in situ leaching uranium mining, is expected in future research.

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.

Identification of Groundwater Potential Zones Using AHP, GIS and RS Integration: A Case Study of Didessa Sub-Basin, Western Ethiopia

Groundwater is a precious water resource in underground rocks and major water source for drinking, industry and irrigation. Identifying the groundwater potential zones in certain areas of Ethiopia is still challenging in terms of time and cost for resolving water scarcity. Groundwater evaluations in Ethiopia are normally done based on field survey. Therefore, this study aims to use the timely and cost-effective remote sensing data and GIS techniques for the evaluation of groundwater potentials in Didessa Sub-Basin from Western Ethiopia. STRM DEM and Landsat-8 satellite image with secondary data was used for this study. Thematic layers showing distribution of slope, lithology, lineament density, drainage density, LULC, soil texture and rainfall were analyzed using weight overlay analysis and AHP algorithm to determine the groundwater potentials. The weights for each element then assigned according to their relative relevance using Saaty’s (AHP) scale. The rainfall and geomorphology showed a higher weight and lithology the lowest. About 4.6% (1289.4km2) of the study area was classified as ‘very high potential, 66% (18532.2 km2) classified as ‘high potential, 27% (7683km2) as moderate potential and 2.1% (587.5 km2) as ‘low’ groundwater potential areas. Therefore, this study demonstrates a robust method, efficient and useful technique for delineating and mapping groundwater potential zones for planning and decision-making for better groundwater management.

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.

DETERMINATION OF GROUNDWATER POTENTIAL AREAS IN KADUNA METROPOLIS, KADUNA, NIGERIA

Groundwater is one of the major resources important for sustainable growth and development. Proper management of the resources is therefore important to meet all the water requirements for human existence to continue. In this study, an investigation is made to explore for groundwater potential areas of Kaduna Metropolis, Kaduna State using remote sensing and GIS technique. The data used for the study are as follows; Landsat 8 (OLI) 30m, Aster data (30m), Geologic map, Soil map and Topographic map was used to generate the factors such as; Land use/cover, Slope, Geomorphology, Geologic rock types, soil textures and drainage density. All the factors and its attributes were weighted and rated respectively and were classified according to their importance to groundwater occurrence using the modified DRASTIC model and the weighted overlay technique was used to create a groundwater potential map of the metropolis. Groundwater potential map were classified into four categories that best describes the potential of each area. These classes are; Poor (21%), Moderate (23%), Good (26%) and Very Good (30.2%) groundwater potential which was found to be concentrated in the central part of the metropolis. This result was verified against existing borehole data and field observations to validate the accuracy and was found to be 76% accurate. The study demonstrated the effectiveness of Remote Sensing and GIS as an effective tool in delineation and identification of groundwater potential areas.

Hydrogeophysical Implication of Geoelectric Sounding at Igarra Comprehensive High School, Akoko Edo Local Government, Nigeria

Electrical resistivity method was used to carry out hydrogeophysical study in order to evaluate the groundwater potential of Igarra Comprehensive High School, Akoko Edo Local Government, Nigeria. The vertical electrical sounding technique (VES) was adopted for the resistivity method. A total of eighteen electrical soundings were conducted across the area using the Schlumberger electrode array with AB/2 varying from 1 to 65 m. After the data acquisition, interpretation was carried out qualitatively and quantitatively and the results were presented as sounding curves, tables, charts, maps and geoelectric sections. The generated geoelectric layers from the sounding curves revealed four geologic layers: the topsoil, the weathered layer, the partially weathered/fractured basement and the fresh basement with their resistivity values ranging from 129.1 to 956.4 -m, 6.8 to 1491.1 -m, 261.3 to 776.6 -m and 1515.6 to 2653.5 -m respectively. The overburden thickness in the study area varies from 5.5 to 23.5 m. The groundwater potential map enabled in the classification of the study area into: low, medium and high groundwater potential area. About 85% of the study area falls within the low groundwater potential rating while about 10% constitutes the medium groundwater potential rating and the remaining 5% constitutes high groundwater potential rating. Keywords: Groundwater, overburden, electrical resistivity, basement, geoelectric sounding.