Search Results (41)

Groundwater constitutes the primary source of liquid freshwater on Earth and is essential for ecosystems, agriculture, and human consumption. However, rising demand, urbanization, and climate change have intensified groundwater depletion, particularly in semi-arid regions. Therefore, assessing groundwater recharge zones is essential for sustainable water resource management in vulnerable areas such as Dera Ismail Khan, Pakistan. This study aims to delineate groundwater potential zones (GWPZs), using an integrated approach combining the Geographic Information System (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). Twelve factors were identified in a study conducted using GIS-based AHP to determine the groundwater recharge zones in the region. These include land use/land cover (LULC), rainfall, drainage density, soil type, slope, road density, water table depth, and remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Moisture Stress Index (MSI), Worldview Water Index (WVWI), and Land Surface Temperature (LST). The results show that 17.52% and 2.03% of the area have “good” and “very good” potential for groundwater recharge, respectively, while 48.63% of the area has “moderate” potential. Furthermore, gentle slopes (0–2.471°), high drainage density, shallow water depths (20–94 m), and densely vegetated areas (with a high NDVI) are considered important influencing factors for groundwater recharge. Conversely, areas with steep slopes, high temperatures, and dense built-up areas showed “poor” potential for recharge. This approach demonstrates the effectiveness of integrating advanced remote sensing indices with the AHP model in a semi-arid context, validated through high-accuracy field data (Kappa = 0.93). This methodology offers a cost-effective decision support tool for sustainable groundwater planning in similar environments. Full article

The rapid urbanization in the Kabul River Basin has increased the demand for water for both drinking and commercial purposes, leading to domestic and industrial water insecurity. Assessing the groundwater potential of the Kabul River Basin is highly crucial for effective water management. The aim of this paper is to identify potential zones for groundwater by employing a Geographic Information System and an Analytical Hierarchy Process approach to formulate a cumulative score based on seven thematic images—rainfall, geology, lineament density, drainage density, land use/land cover, soil type, and slope—within the Kabul River, with assigned weightages of 32%, 27%, 12%, 10%, 8%, 6%, and 5%, respectively, with a consistency ratio of 0.053 (5%), demonstrating the reliability of the results. The study shows that the first three factors contribute more to the percentages of Groundwater Potential Zones. The identified groundwater potential is classified into very good, good, medium, poor, and very poor zones, covering 35.45% (19,989 km²), 37.2% (20,978 km²), 23.16% (13,063 km²), 4.13% (2332 km²), and 0.06% (19 km²), respectively. Groundwater potential in the basin is predominantly classified as good to medium; however, there are notable variations across sub-basins. The Swat sub-basin and western parts of the Kabul River Basin, encompassing the Panjshir and Parwan districts, exhibit exceptionally high groundwater potential. In contrast, the Panjkora sub-basin (Dir district) and southwestern areas of the Kabul River Basin, covering parts of the Ghazni and Wardak districts, have very limited groundwater potential. Full article

The Pandameru River Basin, South India, is affected by high levels of contamination from human activities and the over-exploitation of groundwater for agriculture, both of which pose significant threats to water quality and its availability for drinking and irrigation. To explore sustainable groundwater management, this study presents a machine learning-driven approach to basin-scale groundwater potential zone (GWPZ) mapping by integrating remote sensing (RS), a geographic information system (GIS), and the random forest (RF) algorithm. The research leverages ten thematic layers—including lithology, geomorphology, soil type, lineament density, slope, drainage density, land use/land cover (LULC), NDVI, SAVI, and rainfall—to assess groundwater availability. The RF model, trained with well-distributed groundwater data, provides an optimized classification of GWPZs into five categories: very good (5.84%), good (15.21%), moderate (27.25%), poor (27.22%), and very poor (24.47%). The results indicate that excellent groundwater zones are predominantly located along highly permeable alluvial deposits, whereas low-potential zones coincide with impermeable geological formations and steep terrains. Field validation using piezometric readings and well data confirmed significant variations in water table depths, ranging from 5 m to over 150 m. The groundwater potential map achieved an accuracy of 86%, underscoring the effectiveness of the RF model in predicting groundwater availability. This high-precision mapping technique enhances decision-making for sustainable groundwater management, supporting long-term water conservation, equitable resource allocation, and climate-resilient water strategies. By providing reliable insights into groundwater distribution, this study contributes to the sustainable utilization of groundwater resources in semiarid regions, aiding policymakers and planners in mitigating water scarcity challenges and ensuring water security for future generations. Full article

Overextraction of groundwater, as well as rapidly changing land use patterns, climatic change, and anthropogenic activities, in the densely populated Melur of Tamil Nadu state in India, has led to aquifer degradation. This study maps the groundwater potential (GWPZ) by evaluating 678 km² of this region in the Analytical Hierarchy Processes (AHP) and by using remote sensing and GIS tools as part of SDG 6 for the sustainable management of drinking, irrigation, and industrial uses for future generations. Data information layers, such as aquifer (a), topography (t), lineaments (l), land-use/land-cover (LuLc), soil (s), rainfall (r), and drainage (d) characteristics, separated the study area between poor and excellent groundwater potential zones with 361 km² or 53% of the study area remaining as low GWP and the prospective excellent groundwater potential zone covering only 9 km² (1.3% of total area). The integrated approach of the GWPZ and Water Quality Index (WQI) can effectively identify different zones based on their suitability for extraction and consumption for better understanding. This study also evaluates the performance of three machine learning models, such as Random Forest (RF), Gradient Boosting, and Support Vector Machine (SVM), based on a classification method using the same layers that govern the groundwater potential. The results indicate that both the RF model and Gradient Boosting achieved 100% accuracy, while SVM had a lower accuracy of 50%. Performance metrics such as precision, recall, and F1-score were analyzed to assess classification effectiveness. The findings highlight the importance of model selection, dataset size, and feature importance in achieving optimal classification performance. Results of this study highlight that the aquifer system of Melur has a low groundwater reserve, and it requires adequate water resource management strategies such as artificial recharge, pumping restriction, and implementation of groundwater tariffs for sustainability. Full article

Nowadays, modelling groundwater potential zones (GWPZs) based on scientific principles and modern techniques is a major challenge for scientists around the world. This challenge is even greater in arid and semi-arid areas. Unmanned aerial vehicles (UAVs), geographic information systems (GISs), and multi-criteria decision making (MCDM) are modern techniques that have been applied in various fields, especially in groundwater exploration. This study attempts to apply a workflow for modelling the GWPZs using UAV technology, GIS, and MCDM in semi-arid areas. An aerial survey provided a high-resolution DEM of 4 cm. Six influencing factors, including elevation model, drainage density, lineament density, slope, flood zone, and topographic wetness index, were considered to delineate the GWPZs. Four classes of groundwater potential were identified, namely high (4.64%), moderate (23.74%), low (18.2%), and very low (53.42%). Three validation methods, namely borehole yield data, receiver operating characteristic area under the curve (ROC-AUC), and principal component analysis (PCA), were used and gave accuracies of 82.14%, 65.4%, and 72.49%, respectively. These validations indicate a satisfactory accuracy and justify the effectiveness of the approach. The mapping of GWPZs in semi-arid areas is very important for the availability and planning of water resources management and for sustainable development. Full article

Groundwater is a critical resource in arid regions such as Khamis Mushayt, located in southwestern Saudi Arabia, where surface water availability is limited. This study integrates various geospatial and environmental datasets to delineate groundwater potential zones (GWPZs) using Geographic Information Systems (GISs) and remote sensing (RS) techniques. Key parameters considered include lithology, slope, drainage density, precipitation, soil type, and vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous studies and field data. The results reveal three distinct groundwater potential zones: poor, moderate, and good. Areas with good groundwater potential account for 8.2% of the study area (16.3 km²) and are predominantly located in the eastern and central parts of the study area, in valleys and low-lying regions with permeable geological formations such as alluvial deposits, supported by higher drainage density and favorable precipitation. Conversely, poor-potential zones represent 27.6% (54.50 km²), corresponding to areas with steep slopes and impermeable rock formations. Moderate-potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and account for 64.2% (127.0 km²). Validation using existing well data demonstrates strong agreement between the identified potential zones and actual groundwater availability. These findings provide a strong framework for sustainable water resource management, urban planning, and agricultural development in Khamis Mushayt and similar arid regions. Full article

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article

Groundwater availability in semi-arid regions like the Widyan Basin, the Kingdom of Saudi Arabia (KSA), is a critical challenge due to climatic, topographic, and hydrological variations. The accurate identification of groundwater zones is essential for sustainable development. Therefore, this study combines remote-sensing datasets (Sentinel-2 and ASTER-DEM) with conventional data using Geographic Information System (GIS) and analytical hierarchy process (AHP) techniques to delineate groundwater potential zones (GWPZs). The basin’s geology includes Pre-Cambrian rock units of the Arabian Shield in the southwest and Cambrian–Ordovician units in the northeast, with the Saq Formation serving as the main groundwater aquifer. Six soil types were identified: Haplic and Calcic Yermosols, Calcaric Regosols, Cambic Arenosols, Orthic Solonchaks, and Lithosols. The topography varies from steep areas in the southwest and northwest to nearly flat terrain in the northeast. Hydrologically, the basin is divided into 28 sub-basins with four stream orders. Using GIS-based AHP and weighted overlay methods, the GWPZs were mapped, achieving a model consistency ratio of 0.0956. The zones were categorized as excellent (15.21%), good (40.85%), fair (43.94%), and poor (0%). The GWPZ model was validated by analyzing data from 48 water wells distributed in the study area. These wells range from fresh water to primary saline water, with water depths varying between 13.98 and 130 m. Nine wells—with an average total dissolved solids (TDS) value of 597.2 mg/L—fall within the excellent zone, twenty-one wells are categorized in the good zone, fifteen wells are classified in the fair zone, and the remaining wells fall into the poor zone, with TDS values reaching up to 2177 mg/L. The results indicate that the central zone of the study area is suitable for drilling new water wells. Full article

Groundwater in northwest Tunisia plays a vital role in supporting the domestic, agriculture, industry, and tourism sectors. However, climate change and over-exploitation have led to significant degradation in groundwater quality and quantity. Traditional spatial analysis techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS) are frequently used for assessing groundwater potential and water quality. Yet, these methods are limited by data availability. The integration of Geospatial Artificial Intelligence (Geo-AI) offers improved precision in groundwater potential zone (GWPZ) delineation. This study compares the effectiveness of the Analytical Hierarchy Process (AHP) and advanced Geo-AI techniques using deep learning to map GWPZ in the Majerda transboundary basin, shared between Tunisia and Algeria. By incorporating thematic layers such as rainfall, slope, drainage density, land use/land cover (LU/LC), lithology, and soil, a comprehensive analysis was conducted to assess groundwater recharge potential. The results revealed that both methods effectively delineated GWPZ; however, the Geo-AI approach demonstrated superior accuracy with a classification accuracy rate of approximately 92%, compared to 85% for the AHP method. This indicates that Geo-AI not only enhances the quality of groundwater potential assessments but also offers a reliable alternative to traditional methods. The findings underscore the importance of adopting innovative technologies in groundwater exploration efforts in this critical region, ultimately contributing to more effective and sustainable water resource management strategies. Full article

Groundwater is an essential water resource utilized for agricultural, industrial, and home applications. Evaluating the variability of groundwater is essential for the conservation and management of this resource, as well as for mitigating the reduction in groundwater levels resulting from excessive extraction. This study aimed to define the groundwater potential zones (GWPZ) in Al-Madinah Al-Munawarah, Western Saudi Arabia, utilizing remote sensing and geographic information system (GIS) techniques, alongside meteorological data. Seven thematic maps were produced based on the regulatory characteristics of geology, drainage density, height, slope, precipitation, soil, and normalized difference vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous research and field data. The groundwater potential map was created via the weighted index overlay approach within a GIS. The groundwater potentials were classified into three categories: very poor, moderate, and good zones. The low groundwater potential regions encompass 805.81 km² (44.91%) of the research area, located in mountainous basement rocks, characterized by high drainage density and steep gradients. The moderate zones comprise 45.67% of the total area, covering 819.31 km², and are situated in low-lying regions at the base of mountainous mountains. Conversely, the favorable zones, comprising 9.42% of the total area, span 169.06 km² and are located within the alluvial deposits of the lowlands next to the Wadi Al-Hamd basin and agricultural farms. The results’ accuracy was confirmed by overlaying data from 26 wells onto the designated groundwater potential categories, revealing that all wells corresponded with regions of high groundwater potential. The generated map would contribute to the systematic and efficient management of groundwater resources in this area to meet the rising water demands of Al-Madinah. The groundwater potential map is one aspect of groundwater management. It is also very important to assess this potential further via groundwater temporal monitoring, groundwater balance, and modeling. Full article

Sustainable groundwater development stands out as a contemporary concern for growing global populations, particularly in stressed riverine arid and semi-arid regions. This study integrated satellite-based (Sentinel-2, ALOS-DEM, and CHIRPS rainfall) data with ancillary lithology and infrastructure datasets using Weight of Evidence (WoE) and Frequency Ratio (FR) models to delineate Groundwater Potential Zones (GWPZs) in the Hangu District, a hydrologically stressed riverine region in northern Pakistan, to support the Sustainable Development Goals (SDGs). Ten key variables, including elevation, slope, aspect, distance to drainage (DD), rainfall, land use/land cover, Normalized Difference Vegetation Index, lithology, and road proximity, were incorporated into the Geographic information system (GIS) environment. The FR model outperformed the WoE model, achieving success and prediction rates of 89% and 93%, compared to 82% and 86%. The GWPZs-FR model identified 23% (317 km²) as high potential, located in highly fractured pediment fans below 550 m, with gentle slopes (<5 degrees), DD (within 200 m), and high rainfall in areas of natural trees and vegetation on valley terrace deposits. The research findings significantly support multiple SDGs, with estimated achievement potentials of 37.5% for SDG 6 (Clean Water and Sanitation), 20% for SDG 13 (Climate Action), 15% for SDG 8 (Decent Work and Economic Growth), 12.5% for SDG 9 (Industry, Innovation, and Infrastructure), and notable contributions of 10% for SDG 2 and 5% for SDG 3. This approach provides valuable insights for policymakers, offering a framework for managing groundwater resources and advancing sustainable practices in similar hydrologically stressed regions. Full article

The quality and level of groundwater tables have rapidly declined because of intensive pumping in Punjab (Pakistan). For sustainable groundwater supplies, there is a need for better management practices. So, the identification of potential groundwater recharge zones is crucial for developing effective management systems. The current research is based on integrating seven contributing factors, including geology, soil map, land cover/land use, lineament density, drainage density, slope, and rainfall to categorize the area into various groundwater recharge potential zones using remote sensing, geographic information system (GIS), and analytical hierarchical process (AHP) for Punjab, Pakistan. The weights (for various thematic layers) and rating values (for sub-classes) in the overlay analysis were assigned for thematic layers and then modified and normalized using the AHP. The result indicates that about 17.88% of the area falls under the category of very high groundwater potential zones (GWPZs). It was found that only 12.27% of the area falls under the category of very low GWPZs. The results showed that spatial technologies like remote sensing and geographic information system (GIS), when combined with AHP technique, provide a robust platform for studying GWPZs. This will help the public and government sectors to understand the potential zone for sustainable groundwater management. Full article

The risk of aquifer contamination is determined by the interaction between the pollutant load and the vulnerability of an aquifer. Owing to the decomposition of bodies and degradation of artefacts, cemeteries may have a negative impact on groundwater quality and suitability for use due to the leaching of organic compounds (e.g., biodegradable organics, pharmaceuticals, and formaldehyde), inorganic compounds (e.g., nitrate and heavy metals), pathogenic bacteria, and viruses. Factors such as burial and soil type, rainfall amount, and groundwater depth may increase aquifer vulnerability to pollutants generated in cemeteries. The potential for groundwater contamination was investigated in two cemeteries of the Soure region in Portugal (Samuel–UC9 and Vinha da Rainha–UC10), using the classic DRASTIC model, followed by some adjustments, depending on the particularities of the locations, resulting in a Final Classification considered as Specific DRASTIC. By combining Remote Sensing (RS), Geographic Information System (GIS), and Analytical Hierarchy Process (AHP), groundwater potential zones (GWPZs) were identified, and aquifer vulnerability was assessed, which included the elaboration of thematic maps using GIS operation tools. The maps allowed for the identification of areas with different susceptibilities to contamination: from “Low” to “Very high” for the DRASTIC index and from “Very Low” to “Very high” for the Specific DRASTIC index. Although the difference between the UC9 and UC10 cemeteries is negligible, UC10 is more vulnerable because of its proximity to the community and critically important mineral water resources (such as Bicanho Medical Spa). The Specific model seems better-suited for describing vulnerability to cemeteries. Although there is limited groundwater quality data for the area, the development of vulnerability maps can identify areas that can be sensitive spots for groundwater contamination and establish procedures for pollution prevention. Full article

Groundwater management in the Jinan Spring basin is hampered by its complex topography, overexploitation, and excessive urbanisation. This has led to springs drying up during dry seasons and a decrease in discharge in recent years. GIS and the AHP were employed to delineate groundwater potential zones using eight thematic layers: slope, geology, lineament density, topographic wetness index (TWI), rainfall, soil, drainage density, and land use/land cover (LULC). The model’s accuracy was assessed by comparing the findings to basin groundwater observation well data. We found that 74% of the observations matched the projected zoning. Further validation utilising the receiver operating characteristic (ROC) curve gave an AUC of 0.736. According to the study, 67.31% of the land has a good GWPZ, 5.60% has a very good one, 27.07% is medium, and 0.03% is low. Heavy rains throughout the rainy season raise water levels. Dry weather lowers water levels. This study’s conclusions will protect groundwater from climate change. Integrating hydrogeological data, GIS, remote sensing, and AHP approaches maximises data use, improves groundwater potential zone delineation, and promotes sustainable groundwater resource management decision making. This integrated method can help land use planners, hydrologists, and policymakers find optimal locations for water supply projects, establish groundwater management techniques, and reduce groundwater risks. Full article

The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as “moderate” to “very good” potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management strategies. Full article