Assessing the Vulnerability of Groundwater to Salinization Using GIS-Based Data-Mining Techniques in a Coastal Aquifer

Motevalli, Alireza; Pourghasemi, Hamid Reza; Hashemi, Hossein; Gholami, Vahid

doi:10.1016/b978-0-12-815226-3.00025-9

Cited by 19 publications

(6 citation statements)

References 95 publications

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“…The restoration of groundwater quality is also a very tedious process (Ghaderpoori et al, 2019, Ravindra et al, 2019, Shankar et al,2011a, Thus, there is a need for frequent monitoring of ground water quality and its standards to preserve the water resources for utilization. Verification of water quality will also ensure the reduction in water-borne diseases and development of remedial action for water contamination in the earlier stages (Honarbakhsh et al, 2019, Nair et al, 2019, Motevalli et al, 2019, Zhang et al, 2019b, Shankar et al,2011b. Hence, it is mandatory to add the improvement of fresh water sources together with conservation through effective monitoring as a basic component in the water plan of any developed or developing nation.…”

Section: Introductionmentioning

confidence: 99%

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Balamurugan¹,

Kumar²,

Shankar

et al. 2020

J. Chil. Chem. Soc.

100

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The main objective of this study was to evaluate the groundwater quality for domestic, agriculture use and to describe fluoride contamination in groundwater and their impacts on human health. 67 groundwater samples were collected and analyzed for major ions. Water Quality Index (WQI), Piper diagram and Gibbs diagrams were calculated to measure the suitability of groundwater for drinking purpose. The hazards index value was calculated to estimate the noncarcinogenic risk to adult (male, female) and children suggested by the United States Environmental Protection Agency (USEPA). The irrigation indices were calculated to evaluate the quality of water for irrigation purpose. Statistical methods such as principal component and hierarchical cluster analysis were used to analyses the interrelationship of data. Hydrochemistry of the samples shows, the major ions in the order of Ca 2+ >Mg 2+ >Na + and Cl->SO4in the study area. WQI value of groundwater, 74.62% of sample locations are good and 25.38 % of sample locations need primary treatment for drinking purpose. The results of the hazards index show that 65.67% of the sample locations exceeds the tolerable limit for non-carcinogenic risk (greater than one) for children higher than the risk level for Male and female. Statistical report of PCA and HCA reveals that Ca-Na-HCO3-F has positive loading and TDS-EC has negative loading. The study results show that rock-water interaction and anthropogenic activities are the major factors that influence the quality of groundwater. The continuous intake of excess concentration fluoride causes bone diseases and teeth problems.

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Section: Introductionmentioning

confidence: 99%

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Balamurugan¹,

Kumar²,

Shankar

et al. 2020

J. Chil. Chem. Soc.

100

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“…The weighted suitability analysis is done by using parametric overlay techniques [2] . For the site suitability analysis, the final map is derived by the following Equation (3):…”

Section: Geospatial Datamentioning

confidence: 99%

“…Where to drill a borehole for pumping groundwater in a coastal city? It is challenging and complicated question for town developers, planners, and common public living in coastal regions around the world [1,2] . Centuries ago, people used "dowsing method" [3] .…”

Section: Introductionmentioning

confidence: 99%

Integrated Geophysical and Geospatial Approaches for Delineating Groundwater Potential Zones in Karachi, Pakistan

Khan¹,

Bokhari²,

Nisar³

et al. 2022

eps

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Availability of subsurface fresh water in coastal cities of the world is a growing problem due to sea level intrusion and less seepage. We have utilized an integrated data analysis in which conventional geophysical methods was used to collect primary data for the groundwater resources in Karachi and geospatial approaches were used to model the hydrogeological parameters. It was aimed to investigate geological/hydrogeological conditions of any aquifer system in the study area. The geophysical survey was conducted to acquire electrical resistivity data in the outskirts of Karachi. The geophysical survey was carried out at twenty-one stations by adopting vertical electrical sounding technique with schlumberger configuration. The field data was processed in an iterative process to improve the signal to noise ratio and obtain smooth field data curves for delineation of the aquifer. We have interpreted field data to model the geological information and determine the hydrogeophysical parameters of respective layers. These parameters including the number of layers, aquifer resistivity, aquifer lithology, aquifer thickness and depth to the aquifer, are determined at each field station. The acquired dataset of hydrogeophysical parameters was used to build a geospatial database. The multi-criteria analysis and decision-making process were utilized in GIS based program to model spatial distribution models of hydrogeophysical parameters. The results identified aquifer system in the depth ranging from 53.3 meter to 143.9 meter. The aquifer in the area are mostly sandstone having sufficient thickness which varies from northeast to south and southwest due to undulating topography of the area. The maximum potential of the groundwater is identified in the south which is suitable for water exploration because of low resistivity zone, high aquifer thickness, and flow of drainage network.

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“…Two different algorithms are used in this model, namely boosting and regression, whereby boosting is used to combine several fitting models, and regression is related to the algorithm of classification and regression tree (CART) class [73]. Boosting is a very dominant ML method for improving the model accuracy, and regression is used to categorize the classification system based on the decision trees group [74,75]. The primary disadvantage of using a single decision tree is that it categorizes the training dataset based on a single tree.…”

Section: Importance Of Gecfs By Random Forest (Rf) and Swara Weight Random Forest (Rf)mentioning

confidence: 99%

Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

et al. 2020

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The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

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Assessing the Vulnerability of Groundwater to Salinization Using GIS-Based Data-Mining Techniques in a Coastal Aquifer

Cited by 19 publications

References 95 publications

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Integrated Geophysical and Geospatial Approaches for Delineating Groundwater Potential Zones in Karachi, Pakistan

Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

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