Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India

Kumar, Pankaj; Gopinath, Girish; Seralathan, P

doi:10.1080/01431160601086050

Cited by 209 publications

(26 citation statements)

References 22 publications

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“…In general, drainage density is inversely related to permeability. A high drainage density decreases infiltration and increases surface runoff and is, therefore, not suitable for groundwater development [20]. These high values are concentrated mainly in the Arghen, Ighil, and Berguene rivers.…”

Section: Hydrological Factorsmentioning

confidence: 99%

Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

Benjmel

Amraoui

Boutaleb

et al. 2020

Water

130

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This research work is intended as a contribution to the development of a multicriteria methodology, combining several factors to control the availability of groundwater resources, in order to optimize the choice of location of future drilling and increase the chances to take water from productive structures which will satisfy the ever-increasing water demand of local population (Arghen basin in the Western Anti-Atlas chain of Morocco). The geographic information system is used to develop thematic maps describing the geometry and the hydrodynamic functioning of the aquifer. In this study, 11 factors including geology, topography, and hydrology, influencing the distribution of water resources were used. Based on the Analytical Hierarchy Process (AHP) model, GIS, and remote sensing, the study mapped and classified areas according to their hydrogeological potential. The favorable potential sectors cover 17% of the total area of the basin. The medium potential sectors account for 64%, while the unfavorable areas cover 18% of the basin area. The groundwater potential map of the study area has been validated by comparing with data from 159 boreholes scattered throughout the basin.Water 2020, 12, 471 2 of 16 rural populations [2][3][4]. The aquifers that form in this type of context generally consist of crystalline rocks of plutonic and metamorphic origin (gneiss, schists, micaschists, etc.). These are hard rocks that expose relatively homogeneous mechanical behavior and overall hydraulic properties and are characterized mainly by fracture permeability [5]. Conventional research and prospection methods (hydrochemical, geophysical, etc.) often take a long time, require a lot of financial resources, and need qualified personnel [6][7][8]. In recent decades, several studies have shown that the analytical hierarchy process (AHP), based on geographical information systems (GIS) and spatial remote sensing, offers good functionality for mapping the groundwater recharge area [7][8][9][10][11].The main objective of this study was to evaluate the ability of AHP and GIS techniques to map potential groundwater areas, to provide policy makers and managers with a guide map for hydrogeological research, to guide the implementation of future points of water, and consequently reduce the costs of hydrogeological investigation.

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Section: Hydrological Factorsmentioning

confidence: 99%

Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

Benjmel

Amraoui

Boutaleb

et al. 2020

Water

130

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“…Rainfall is one of the most important factors in groundwater potential model because the more precipitation region are likely to have more groundwater potential [9]. Opposite to rainfall, river density has a reverse relationship with groundwater potential [32] because when the drainage density is lower, the infiltration and recharge are greater [29]. Land use presents the influence of human activities on the landscape evolutions [33][34][35].…”

Section: Data Usedmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

107

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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“…This factor was classified into five classes: <2.92, 2.92-3.84, 3.84-4.69, 4.69-6.57 and >6.57 (Figure 3g). An area's drainage system relies on the nature and composition of geological formations, ability to absorb soil, permeability and slope [34]. High drainage density increases surface runoff and decreases infiltration.…”

Section: Hydrological Parametersmentioning

confidence: 99%

Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2019

Water

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In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m3/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map.

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Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India

Cited by 209 publications

References 22 publications

Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

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