Identification Of Groundwater Potential Zones Research Articles

Mapping of Groundwater Potential Zones in the Kuzhithuraiyar Sub Basin of Kodayar River, Kanniyakumari District, Tamilnadu: Using Analytic Hierarchy Process (AHP) and GIS


 
 
 Groundwater plays a very important role for all living beings. It is decisive to have a scientific understanding of groundwater management since, with careful use and replenishment, groundwater may help solve issues. The distribution of groundwater tables, slope, land-form, drainage pattern, lithology, topography, geological structure, fracturing density, opening and connectivity of fractures, secondary porosity, and landuse landcover all affect the occurrence and efficiency of groundwater in an aquifer system. An essential tool for assessing, tracking and protecting groundwater resources is the integration of geospatial techniques such as Remote Sensing and Geographic Information System for the identification of groundwater potential zones. This study aims to find the groundwater potential zones using Analytical Hierarchy Process (AHP) and managing the resources by creating different thematic layers such as rainfall, geology, geomorphology, drainage density, soil, slope, lineament and landuse landcover (LULC) of the Kuzhithuraiyar sub basin of the Kodayar river in Tamilnadu using the application of geospatial technologies. The thematic maps for all the thematic layers have been prepared using tools such as Interpolation, contour lines, Classification in ArcGIS 10.8. Theme weight and class rank were assigned to different thematic layers in weighed overlay analysis. The results were validated through field work and groundwater potential map was created. The groundwater potential zonation mapping was done by the overlay analysis in ArcGIS 10.8 software. The obtained map was classified into four categories namely very high groundwater potential zone, high groundwater potential zone, medium groundwater potential zone and low groundwater potential zone. The North Eastern part of the basin is considered as low groundwater potential zone while the Southern portion has high groundwater potentiality. The low groundwater potential zone covers an area of 10.58 sq.km and high groundwater potential zone covers an area of about 388.37sq.km.This study will be helpful for useful groundwater management for different tenacities.
 
 

Identification of groundwater potential zones and its relation to settlement and agriculture: a study in Taraphini river basin, West Bengal, India

Identification of groundwater potential zones and its relation to settlement and agriculture: a study in Taraphini river basin, West Bengal, India

Identification of groundwater potential zones using integrated GIS-based analytical hierarchy process and multicriteria decision analysis methods in Jedeb watershed, Ethiopia

Identification of groundwater potential zones using integrated GIS-based analytical hierarchy process and multicriteria decision analysis methods in Jedeb watershed, Ethiopia

Identification of groundwater potential zones using geospatial techniques and analytical hierarchy process (AHP): case of the middle and high Cheliff basin, Algeria

Identification of groundwater potential zones using geospatial techniques and analytical hierarchy process (AHP): case of the middle and high Cheliff basin, Algeria

Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning

Groundwater is a critical resource, yet its detailed assessment in mountainous regions is challenged by varying topography, complex hydrogeological characteristics and limited data. In this study, machine learning approaches were used to analyze the groundwater potential in five different watersheds in Nepal. Explainable machine learning models (EBM and GAMI-net) were used to identify zones with different groundwater potentials and controlling factors. The models were validated with k-fold cross-validation using the area under the receiver operating characteristics curve for the two groundwater potential models with unseen validation dataset of 0.87 and 0.88 respectively. We found that precipitation, elevation, soil bulk density, slope and lineaments primarily controls the groundwater potential in the study regions. The expected impact of each of the factors on groundwater potential was complex and multimodal. The results of this study can be used to improve water resource management and ensure sustainable groundwater use in the region.

Identification of groundwater potential zones of Idukki district using remote sensing and GIS-based machine-learning approach

Abstract Kerala's Idukki district, which is situated on the Western Ghats of India, is susceptible to flooding and landslides. As a result of the 2018 Kerala floods, this disaster-prone region experienced drought conditions. In order to lessen the effects of future disasters, it is also necessary to identify and evaluate the district's groundwater potential (GWP). This work used three machine-learning (ML) algorithms – Random Forest (RF), Adaptive Boosting (AdaBoost), and Gradient Boosting (GB) – to model and produce GWP zonation maps for the Idukki district. Fourteen conditioning factors including elevation, slope, curvature, Topographic Roughness Index, lineament density, soil, geology, geomorphology, Topographic Wetness Index, Sediment Transport Index, drainage density, rainfall, land-use/land-cover (LULC), and Normalised Difference Vegetation Index were adopted as input parameters in the modelling. All showed prominence when they were examined for feature importance using the recursive feature elimination (RFE) method. The RF model outperformed the other two ML models in terms of fit, with an area under curve (AUC) value of 0.92, while the GB and AdaBoost models displayed less fit, with AUC values of 0.90 and 0.88, respectively. GWP maps produced by each model were reclassified into five zones – very high to very low – it was discovered that the zones were evenly spread throughout the Idukki region.

Identification of groundwater potential zones in the Rabigh-Yanbu area on the western coast of Saudi Arabia using remote sensing (RS) and geographic information system (GIS)

The groundwater potential zones in the Rapigh-Yanbu area, on the west coast of Saudi Arabia, were evaluated using remote sensing and geographic information system data leading to the establishment of major watershed development and management strategies. Many factors, including slope, drainage density, land use, geology, lineament density, elevation, yearly precipitation, and soil type distribution, affect the hydrogeological characteristics of coastal aquifers. The Rabigh-Yanbu coastline groundwater potential zones were located and mapped using these characteristics. The annual precipitation rate is categorized into 2–28 mm, 29–52 mm, 53–76 mm,77–107 mm, and 108–164 mm within the study area. The highest elevation was ordered with the lowest rank of 1, while the lowest elevation was given the highest rank of 5. Slopes of 0°–9° were present in the majority of the study region, while slopes of 9°–63° were present in the northwest. Based on the slope, the research area was separated into five classes; 0-3o; 4-9o; 10–16o; 17–25o; 26–63o. Based on the drainage density map; 0–0.36 km2, 0.37–0.53 km2, 0.54–0.73 km2, 0.74–1.32 km2, 1.33–3.31 km2. The majority of the fractures trended NE–SW and NW–SE. A map of lineament density was made using the line density method. This map makes it obvious that the region was divided into five classes; 0–0.02 km2, 0.03–0.08 km2, 0.09–0.13 km2, 0.14–0.2 km2, and 0.21–0.34 km2. The classification of soil types in the study region into four classes such as clay loam, clay, sandy clay, and loam. Accordingly, the clay soil was ranked 1, while the loam soil was given the highest rank of 4. The Precambrian rocks had low groundwater potential because of their reduced porosity and were ranked 1, while Quaternary deposits had a high porosity and good groundwater potential and were given a high rank of 3. According to the likelihood of groundwater supply, the area is divided into four categories: very good, good, moderate, and poor. The area with very good groundwater potential covered 1,384 km2, while the area with good groundwater potential covered 30,498 km2, according to the findings. The study area had 34,412 km2 of somewhat appropriate land for groundwater development. In contrast, 1,734 km2 of the study area is scarce. The best zones featured the lowest slopes, the least number of lineaments, and the most drainage. The lineaments, which serve as the main conduits for groundwater flow and storage, have significant levels of groundwater recharge capacity in the study area. Managing groundwater according to these identified potential zones will maximize their benefits and increase the groundwater yield. This will help in creating new agricultural, urban, and industrial communities in this strategic area and contribute toward achieving the goals of Saudi Vision 2030.

Identification of Groundwater Potential Zones using geospatial techniques in the Varattar sub-basin, Dharmapuri district, South India


 
 
 An attempt has been made to map groundwater potential zones by integration of spatial data using remote sensing and GIS techniques. The input data for this analysis are different parameters that can be considered in identifying the groundwater potential zone, such as drainage density, geology, slope, lineament density, geomorphology, land use / land cover and soil, generated using remote sensing, topographical and secondary data. These parameters are converted to raster format and assigned scores and weightings computed from multi influencing factor (MIF) technique. The weighted parameters are statistically computed and integrated to obtain the groundwater potential zones in the Varattar sub basin. The final integrated groundwater potential zones are classified into five categories like very low, low, moderate, high and very high. The delineated groundwater potential map is validated with the well location data using the R-index method. This groundwater potential zone map will be useful for extraction and manage- ment of groundwater in the study area.
 
 

Identification of Groundwater Potential Zones in Samendeni Watershed in Sedimentary and Semi-Arid Contexts of Burkina Faso, Using Analytic Hierarchy Process (AHP) Method and GIS

Demand for water increases in Samendeni regarding the undertaken agricultural projects while pressure on surface water from global warming/evapotranspiration also increases. Thus, the need to evaluate the groundwater potential in the catchment is crucial as alternative supplier of water and resilience to climate hazards. The AHP was performed integrating ten influencing factors such as geomorphology, geology, soil, land use/land cover (lulc), slope, rainfall, drainage density, borehole rate & depth and piezometric level to generate groundwater potential zones (GWPZs) in Samendeni watershed (4420 km2). All the factors were processed and ranged into five (5) classes. Weight was assigned to each class of thematic layer. These thematic layers were then reclassified based on the normalized weight to be used in the calculation of groundwater potential zones (GWPZ). The final output, groundwater potential map, revealed a significant groundwater potential with very good (11%), good (31%), moderate (30%), poor (20%), and very poor (8%) of proportion. The interesting (very good, good) GWPZs in the study area are mostly in the central towards the east. The poor zones in term of groundwater potential are concentrated in the upper west region of the watershed. Besides the cross-validation with the relationship between different groundwater potential zones and the wells available in the study area, the overall accuracy was estimated to 88% provided from the result of the similarity analysis where 22 out of the 25 validation wells match with the expected yield classes of GWPZs. The statistics from that validation revealed the performance of AHP method to delineate groundwater potential zones at catchment level.

Identification of Groundwater Potential Zones of Catchment Using Geospatial Technology

Identification of Groundwater Potential Zones of Catchment Using Geospatial Technology

Identification of groundwater potential zones in a tropical lateritic terrain by analytical hierarchy process-based multi-criteria analysis and geospatial technology

Identification of groundwater potential zones in a tropical lateritic terrain by analytical hierarchy process-based multi-criteria analysis and geospatial technology

Identification of Groundwater Potential Zones Using Remote Sensing and GIS for Gautam Buddha Nagar

Abstract: With an escalatory growth of human population in the 21st century there has been a significant increase in demand of water for human survival. With the limitation of Surface water sources now ground water that contributes to the total annual supply of water consumption and outshines as one of the major sources. The objective of this paper is to review and analyze the area of Gautam Buddh Nagar, Uttar Pradesh using remote sensing and Geographic Information System for identification of groundwater potential zones. There is various expertise which are used for different water zones for its mapping and its analysis. Parameters like density, soil, land use\cover, geology, drainage, rainfall, contour and terrain are used for controlling groundwater zones. These Groundwater mapping methods are explained and extracted from data sources of satellite images. These techniques include methods which are advance and conventional. For identification and mapping of groundwater potential analysis the thematic layers are used. The significance of each layer is discussed for the location groundwater potential zones using the conditions present. For effective exploration and identification of appropriate locations for water extraction this groundwater potential information will be helpful. For analysis exploration and study of a location remote sensing data is a fast, cost-effective and economical way of doing it. With the Integration of these data for the exploration of groundwater resources has been seen as a breakthrough in the field of its research, and plays a significant role for providing assistance in monitoring, accessing, and preservation of groundwater resources. In the present paper, the assessment of groundwater availability in Gautam Buddha Nagar, Uttar Pradesh, India have been conducted using these techniques. Various maps were prepared like base map, DEM, drainage density map, contour map, land use map, lineament density map and groundwater potential zones using the data of remote sensing and the existing maps. Raster data was transformed from base map and DEM using feature to raster converter tool in ArcGIS 10.3 version

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

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

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

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

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.

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

In the field of groundwater engineering, a convolutional neural network (CNN) has become a great role to assess the spatial groundwater potentiality zones and land use/land cover changes based on remote sensing (RS) technology. CNN can be offering a great potential to extract complex spatial features with multiple high levels of generalization. However, geometric distortion and fuzzy entity boundaries as well as a huge data preparation severance may be the main constraint and affect the spatial potential of CNN application for land cover classification. This study aims to recognize the proficiency of deep learning algorithms, i.e., CNN, for spatial assessment of groundwater potential zones and land cover. Among the groundwater influencing factors, classification of land cover (agriculture, built-up, water bodies, forests, and bare land) has been reported by several researchers for different purposes and they approved the CNN capability for the prediction of spatial groundwater potentiality zones like very high, high, moderate, poor, and very poor areas. In this study, CNN is recommended as a very essential algorithm for the identification of groundwater potential zones and classification of land use/land cover change. CNN gives a better option for scholars regarding when the limited data sets are available for validation.

Identification of groundwater potential zones in the Southern Mountains, Yogyakarta Special Region

Southern Mountains, Yogyakarta Special Region is a zone that extends from the south-eastern part of Yogyakarta Special Region to the east along the southern coast of East Java. From the hydrogeological map, it is known that the area is an area of scarce groundwater. This study aims to determine the potential for groundwater in these areas. The identification of groundwater potential is carried out using the Groundwater Potentiality Index (GPI) method by referring to 5 determining parameters: lineaments, lithology, drainage, topography, and rainfall. Lineament, drainage, and topography data were obtained from the Indonesia Geospatial Portal and the Digital Elevation Model (DEM) belonging to the Indonesian Geospatial Information Agency. The lithological data is obtained from data from the Geological Survey Centre, Geological Agency, Ministry of Energy, and Mineral Resources in the form of Geological Maps of Remote Sensing Image Interpretation Results. Rainfall data were obtained from reports from the Serayu Opak River Basin Office and the Public Works, Housing, and Energy Department of Mineral Resources Yogyakarta Special Region. The evaluation of parameters reveals that even in the scarce groundwater zones, estimation of area with possible of groundwater can be classified into five classes of groundwater potential index, namely: very low (<56.25), low (56.25-90), medium (90-123.75), high (123.75-157.5), and very high (> 157.5). In the next step of research the validation may be conducted on a high and very high GPI value whether their are really is associated with the presence of springs and wells. If it is correlated, future exploration of groundwater in this area will be focused and successful.

Identification of groundwater potential zones in Kabul River Basin, Afghanistan

Groundwater (GW) plays a vital role in the socio-economic growth of Kabul River Basin (KRB) in Afghanistan. Since the GW resources in the basin have not been properly managed, there is a need for sound strategies by first identifying the potential GW zones. This study assesses the potential groundwater zones for the KRB using the Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). In this direction, seven different thematic maps of rainfall, lithology, land use/land cover, slope, soil, drainage density, and lineament density are first prepared using the GIS. The AHP is then employed to assess the weights of different themes. Finally, the weighted overlay option in the GIS is used to generate the map of the groundwater potential zones (GWPZ). The Very Good zones are mostly located in the downstream and central parts of the KRB, covering around 1543 km2 area. The Good and the Poor zones are found to be randomly distributed, covering about 39 444 km2 and 27 658 km2, respectively. The Very Poor zones are located in the west, southwest, and in some central parts of the basin, covering about 2272 km2. It is found that only 18% of the total average annual precipitated water of 6.88 × 109 m3/year infiltrates into the subsurface and ultimately contributes to recharging of the groundwater.

Evaluation of data-driven model and GIS technique performance for identification of Groundwater Potential Zones: A case of Fincha Catchment, Abay Basin, Ethiopia

Study regionFincha Catchment, Abay Basin, Ethiopia. Study focusThe study evaluates the performance of the data-driven (ANN) model and GIS technique for the exploration of groundwater potential zones. Lineament density, drainage density, slope, LULC, rainfall, soil, geology, and geomorphology were used as the criteria for the delineation of groundwater potential zones. The criteria were ranked and weighted in the ANN and AHP approaches. Weighted overlay analysis in the GIS platform was used in both methods to delineate the potential zones. The accuracy of the delineated groundwater potential zones in both cases was evaluated by the ROC curve developed from the pumping rate. New hydrological insights for the regionFive and four classes of groundwater potential zones were delineated in ANN and GIS respectively. This indicates that the performance of the ANN model in delineating the groundwater potential zones was better than the GIS technique. The accuracy of the predicted potential zones was evaluated and AUC computed from the ROC curve in both methods revealed an agreement of 96 % and 91 % were made between the ground-truthing points and the ANN model and between GIS platforms respectively. The performance of the ANN was better than the GIS technique in delineating the potential zones. Finally, it is concluded that the ANN model is an effective tool for the delineation of groundwater prospective zones.

Identification of groundwater potential zones using AHP in district Kuala Krai, Kelantan, Malaysia

Groundwater is one of the main sources of water in Kelantan which consumes 70% of the water supply. The rapid development in population, urbanization, agriculture, and industry has led to an increased water supply and demand. Hence, it is necessary to identify new groundwater potential sources to enhance groundwater supply in the study area. This study was carried out at Kuala Krai, Kelantan within latitude 5”36’30° to 5”33’50° N and longitude 102”8’25° to 102”11’10° E. The Analytical Hierarchy Process (AHP) method were used in this study and this method eventually come out with groundwater potential zones. The parameters such as lithology, geomorphology, land use, slope, drainage density, rainfall density, and soil type were chosen and spatially generated using GIS platform. Those parameters were subjected to weighted overlay analysis to obtain the potential zones of groundwater. The weights for the various layers were generated and assigned using the AHP technique which allows the comparison of criteria influencing the potential zone. The results of the study revealed that the good potential zone comprises 36.1%, moderate 32.4%, and poor 31.5% areas respectively. In conclusion, factors such as low elevation, gentle slope, alluvial plains, and vegetation area have given a high potential of groundwater in this study area.