Groundwater prospect mapping using remote sensing, GIS and resistivity survey techniques in Chhokra Nala Raipur district, Chhattisgarh, India

Indhulekha, K; Mondal, K. C.; Jhariya, D. C.

doi:10.2166/aqua.2019.159

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…Finally, the following factors were generated: Generation of Land Use/Land-Cover Classification and Accuracy Assessment Many studies have underlined the importance of LULC in the hydrological interpretation for the development of GW potential [44,49,50]. The presence and productivity of GW in a particular area as well relies upon the soil surface features, which act as a mediator in the process of "runoff-infiltration and runoff-evapotranspiration" [51]. Hence, to detect LULC variation for the Hesare-No Basin, multi-spectral bands of Landsat-8 (OLI) images and three supervised classification algorithms, i.e., maximum likelihood, neural network, and decision tree, were implemented.…”

Section: Rs-derived Factors Satellite Data and Pre-processingmentioning

confidence: 99%

“…The distance from lineaments is beneficial for GW potential assessment as the target hydrogeological zones that are located adjacent to linear structures control the movement and storage of GW [13,56]. It also describes surface morphologies such as "faults, fractures, cracks," as well as provides information on the infiltration of water depending on rock properties [51]. The distance from lineaments in the Hesare-No Basin was provisioned from the Landsat-8 OLI image as shown in Figure 5c.…”

Section: Distance From Lineament and Lineament Densitymentioning

confidence: 99%

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Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

et al. 2020

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Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

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Section: Rs-derived Factors Satellite Data and Pre-processingmentioning

confidence: 99%

Section: Distance From Lineament and Lineament Densitymentioning

confidence: 99%

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

et al. 2020

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“…The AHP is a concept of measurement by pairwise comparison matrix (PCM) and is based on the judgment of experts to derive priority weights [74]. The worldwide academic network progressed the integrated GIS and AHP technique to investigate complex spatial issues [50,56,72,74,75,80,81]. PCM for thematic layer, viz.…”

Section: Assignment Of Weight and Weight Normalizationmentioning

confidence: 99%

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

Pathmanandakumar

Thasarathan

Ranagalage

2021

IJGI

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The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the main objective of this study is to delineate potential groundwater zones in Kilinochchi, Sri Lanka, using integrated remote sensing (RS), geographic information systems (GIS) and the analytical hierarchy process (AHP). Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. The thematic layers were integrated into a geographic information system, and a weighted overlay analysis was carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low (59.12 km2), low (207.78 km2), moderate (309.89 km2), high (507.74 km2), and very high (111.26 km2). The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This suggests that the results obtained by integrating RS-GIS and AHP are well-matched with the existing well water depth. The AHP approach based on RS-GIS was a handy and efficient technique for assessing potential groundwater zones. This research will help policymakers better manage the Kilinochchi district’s groundwater resources and give scope for further research into groundwater exploration in the area.

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“…The AHP is a concept of measurement by pair-wise comparison matrix (PCM) and is based on the judgment of experts to derive priority weights [26]. The worldwide academic network progressed the integrated GIS and AHP technique to investigate complex spatial issues [1,9,11,12,15,26,29]. PCM for thematic layer viz.…”

Section: Assignment Of Weight and Weights Normalizationmentioning

confidence: 99%

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

Pathmanandakumar¹,

Thasarathan²,

Ranagalage³

2021

Preprint

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The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the existing groundwater resources should be managed to overcome the situation. Several authors worldwide have published studies on the delineation of potential groundwater zone. However, only a few studies addressed the delineation of potential groundwater zones in the Kilinochchi district. This study aims to delineate potential groundwater zones in Kilinochchi, Sri Lanka using integrated Remote Sensing, Geographic Information Systems, and Analytic Hierarchy Process techniques. Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. Saaty's scale was applied to the assigned weights of the chosen thematic layers and their features. The thematic layers were integrated into a Geographic Information System, and a weighted overlay analysis is carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low, low, moderate, high, and very high. It was found that the very high groundwater potential zone is mainly found in the north-eastern part of the study area covering 111.26 km2. The upper north-western, middle, and eastern parts of the study area fall within the high groundwater potential zone covering about 507.74 km2. The moderate groundwater potential zones (309.89 km2) mainly occurred in the western part, and the extreme west part of the study area falls under low (207.78 km2) and very low (59.12 km2) zones. The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This research will help policymakers better manage the Kilinochchi district's groundwater resources and gives scope for further research into groundwater exploration in the area.

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Groundwater prospect mapping using remote sensing, GIS and resistivity survey techniques in Chhokra Nala Raipur district, Chhattisgarh, India

Cited by 17 publications

References 18 publications

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

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