Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India

Saravanan, Subbarayan; Pitchaikani, S; Thambiraja, M; Sathiyamurthi, S.; Sivakumar, Vivek; Velusamy, Sampathkumar; Shanmugamoorthy, Manoj

doi:10.1007/s10661-022-10601-y

Cited by 17 publications

(2 citation statements)

References 55 publications

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“…Considering the correlation analysis results, it was seen that the AHP-DRASTICLu model provides more accurate results than the DRASTIC model in terms of the sulfate and chloride contents of the groundwater except for the electrical conductivity in the UKV. In many studies conducted to determine the groundwater vulnerability to pollution, the nitrate [31,100,101], sulfate [102][103][104], and chloride [29,105] concentrations of groundwater were used to validate the model results. It was seen that the vulnerability index values were highly correlated with sulfate in some areas [106,107], chloride in some areas [35,108], and nitrate in some areas [36,109].…”

Section: Models' Comparison and Validationmentioning

confidence: 99%

Evaluation of Groundwater Vulnerability in the Upper Kelkit Valley (Northeastern Turkey) Using DRASTIC and AHP-DRASTICLu Models

Yıldırım

2023

IJGI

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This study aimed to investigate groundwater vulnerability to pollution in the Upper Kelkit Valley (NE Turkey). For this purpose, vulnerability index maps were created using the generic DRASTIC and AHP-DRASTICLu models. The latter model was suggested by adding a parameter to the DRASTIC model and weighting its parameters with the analytical hierarchy process with the GIS technique. The results showed that areas with high and very high vulnerabilities are concentrated around the Kelkit Stream, which flows from east to west in the central part of the study area. In contrast, areas with low and very low vulnerability classes are located in the northern and southern parts of the study area. To validate the model results, a physicochemical characterization of groundwater samples and their corresponding vulnerability index values were statistically compared using the Spearman correlation method. In addition, the single-parameter sensitivity method was applied to analyze the models’ sensitivities. Results revealed a stronger correlation between the vulnerability index values of the AHP-DRASTICLu model (compared to the DRASTIC model) in terms of sulfate (R2 = 0.75) and chloride (R2 = 0.76), while there was a slightly weaker correlation for the electrical conductivity (R2 = 0.65) values of the groundwater samples. Sensitivity analysis indicated that the vadose zone, aquifer media, and land use are the most influential parameters responsible for the highest variation in the vulnerability index. Generally speaking, the results indicated that the AHP-DRASTICLu model performs better than the DRASTIC model for investigating groundwater vulnerability to pollution in the Upper Kelkit Valley.

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Section: Models' Comparison and Validationmentioning

confidence: 99%

Evaluation of Groundwater Vulnerability in the Upper Kelkit Valley (Northeastern Turkey) Using DRASTIC and AHP-DRASTICLu Models

Yıldırım

2023

IJGI

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“…In cases where an indicator is categorized into three ranges, its ratings were adjusted to 0.1, 0.3, and 0.7. The delineation of factor ranges and assignment of ratings are derived from previous studies [94][95][96][97][98]. The ranges and ratings of all indicators are presented in Table 2.…”

Section: Assessment Indicatorsmentioning

confidence: 99%

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Huang,

Wu,

et al. 2023

Remote Sensing

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Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.

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Groundwater vulnerability assessment using AHP-DRASTIC-GALDIT comprehensive model: a case study of Binhai New Area, Tianjin, China

Luo

Qiu

et al. 2023

Environ Monit Assess

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Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India

Cited by 17 publications

References 55 publications

Evaluation of Groundwater Vulnerability in the Upper Kelkit Valley (Northeastern Turkey) Using DRASTIC and AHP-DRASTICLu Models

Evaluation of Groundwater Vulnerability in the Upper Kelkit Valley (Northeastern Turkey) Using DRASTIC and AHP-DRASTICLu Models

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Groundwater vulnerability assessment using AHP-DRASTIC-GALDIT comprehensive model: a case study of Binhai New Area, Tianjin, China

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