Groundwater vulnerability and contamination risk mapping of semi-arid Totko river basin, India using GIS-based DRASTIC model and AHP techniques

An integrated groundwater vulnerability and artificial recharge site suitability assessment using GIS multi-criteria decision making approach in Kayseri region, Turkey

Groundwater resources worldwide face significant challenges that require urgent implementation of sustainable measures for effective long-term management. Managed aquifer recharge (MAR) is regarded as one of the most promising management technologies to address the degradation of groundwater resources. However, in urban aquifers, locating suitable areas that are least vulnerable to contamination for MAR implementation is complex and challenging. Hence, the present study proposes a framework encapsulating the combined assessment of groundwater vulnerability and MAR site suitability analysis to pinpoint the most featured areas for installing drywells in Kayseri, Turkey. To extrapolate the vulnerable zones, not only the original DRASTIC but also its multi-criteria decision-making (MCDA)-based modified variants were evaluated with regard to different hydrochemical parameters using the area under the receiver operating characteristic (ROC) curve (AUC). Besides, the fuzzy analytical hierarchy process (FAHP) rationale was adopted to signify the importance level of criteria and the robustness of the framework was highlighted with sensitivity analysis. In addition, the decision layers and the attained vulnerability layer were combined using the weighted overlay (WOA). The findings indicate that the DRASTIC-SWARA correlates well with the arsenic (AUC = 0.856) and chloride (AUC = 0.648) and was adopted as the vulnerability model. Groundwater quality parameters such as chloride and sodium adsorption ratio, as well as the vadose zone thickness, were found to be the most significant decision parameters with importance levels of 16.75%, 14.51%, and 15.73%, respectively. Overall, 28.24% of the study area was unsuitable for recharge activities with high to very high vulnerability, while the remaining part was further prioritized into low to high suitability classes for MAR application. The proposed framework offers valuable tool to decision-makers for the delineation of favorable MAR sites with minimized susceptibility to contamination.

Modelling groundwater vulnerability in a vulnerable deltaic coastal region of Sundarban Biosphere Reserve, India

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world's largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. "logistic regression (LR)", "random forest (RF)", and "boosted regression tree (BRT)", were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

A modified DRASTIC model for groundwater vulnerability assessment using connecting path and analytic hierarchy process methods

Groundwater plays a vital role in supporting water for the different needs of domestic, agricultural, and industrial sectors, and its vulnerability assessment to pollution is a valuable tool for establishing protective and preventive management. DRASTIC is a well-known GIS-based model for assessing groundwater vulnerability to pollution, which uses seven parameters including depth-to-water level, net recharge, aquifer media, soil media, topography, the impact of the vadose zone, and hydraulic conductivity. The predefined weights of DRASTIC parameters have made a barrier to its applicability for different regions with different hydroclimatic conditions. To overcome this problem, it has been suggested to apply analytic hierarchy process (AHP) method for modifying the model by adjusting the weights of the parameters. AHP is a widely used method to elicit experts' judgments about different involving parameters through constructing pairwise comparison matrixes (PCMs). Since AHP calculates the weights by performing pairwise comparisons between the parameters, achieving consistent comparisons is difficult when the number of parameters increases. The objective of this research is to modify the DRASTIC model by integrating the connecting path method (CPM) and AHP. The proposed methodology involves asking experts to perform a number of pairwise comparisons between the parameters and then construct an incomplete PCM using the obtained information. To complete the missing values in the PCM, CPM is employed. The CPM is an effective approach that not only estimates missing judgments but also ensures minimal geometric consistency. The proposed method along with DRASTIC and pesticide DRASTIC models is applied to Khoy County, which is located in the northwest part of Iran. The efficiency of the proposed method was further confirmed through the results of the Pearson coefficient test conducted on nitrate concentrations. The test revealed correlation values of 0.47, 0.27, and 0.57 for DRASTIC, pesticide DRASTIC, and modified DRASTIC, respectively. These results demonstrated that the proposed method provides a more precise evaluation of groundwater vulnerability.

Using appropriate Kappa statistic in evaluating inter-rater reliability. Short communication on "Groundwater vulnerability and contamination risk mapping of semi-arid Totko river basin, India using GIS-based DRASTIC model and AHP techniques"

In this article, some misuses of Kappa statistic in the original paper [Chemosphere, 307, 135831] are discussed. By using DRASTIC and Analytic Hierarchy Process (AHP) models, the authors have assessed the groundwater vulnerability of Totko, India. High nitrate concentrations in groundwater have been found in highly vulnerable areas, and the accuracy of the models has been assessed through Pearson's correlation coefficient and Kappa coefficient. However, using Cohen's Kappa to estimate the intra-rater reliabilities (IRRs) of the two models is not appropriate on the condition of ordinal categorical variables in five categories in the original paper. We briefly introduce the Kappa statistic and propose to use weighted Kappa to compute IRRs under such conditions. To conclude, we recognize that this does not significantly alter the conclusions of the original paper, but it is necessary to ensure that the appropriate statistical tools are used.

Spatio-temporal variation of groundwater pollution in urban wetlands and management strategies for zoning

Groundwater is an important resource to maintain the sustainable development of urban wetlands. The Jixi National Wetland Park (JNWP) was studied to realize the refined prevention and control of groundwater. The self-organizing map-K-means algorithm (SOM-KM), improved water quality index (IWQI), health risk assessment model and forward model were used comprehensively to evaluate the groundwater status and solute sources in different periods. The results showed that the groundwater chemical type in most areas was the HCO₃-Ca type. Groundwater chemistry data from different periods were clustered into five groups. Groups 1 and 5 are affected by agricultural and industrial activities, respectively. The IWQI value in the normal period was higher in most areas due to the influence of spring ploughing. The east side of the JNWP was disturbed by human activities, and the quality of drinking water continued to deteriorate from the wet period to the dry period. 64.29% of the monitoring points showed good irrigation suitability. The health risk assessment model showed that the health risk was the largest in the dry period and the smallest in the wet period. The main factors causing health risks in the wet period and other periods were NO₃^- and F^-, respectively. The overall cancer risk was within acceptable limits. The forward model and ion ratio analysis showed that the weathering of carbonate rocks was the main factor affecting the evolution of groundwater chemistry, accounting for 67.16%. The high-risk areas of pollution were mainly concentrated in the east of the JNWP. K⁺ and Cl^- were the key monitoring ions in the risk-free zone and potential risk zone, respectively. The research can be used to help decision-makers carry out fine zoning control of groundwater.

Integrated hydrogeophysical and GIS based demarcation of groundwater potential and vulnerability zones in a hard rock and sedimentary terrain of Southern India

This research has identified the groundwater potential and vulnerability zones in Tiruchirappalli district of Tamil Nadu, India. The Schlumberger electrode array has been used to conduct vertical electrical sounding (VES) at 95 sites with a maximum electrode spacing of 150 m. The study area comprises of hard rock and sedimentary formations. Geographical Information System (GIS) has been used to integrate the geoelectrical data and to prepare spatial variation maps for various parameters. Finally, groundwater potential and vulnerability zones have been demarcated, and these outputs have been validated using water level and nitrate data, respectively. The Dar-Zarrouk parameters such as longitudinal conductance (S), transverse unit resistance (T), and aquifer anisotropy (λ) have been used along with the spatial variation of resistivity and aquifer thickness to find out groundwater potential areas with the support of GIS. The thickness of topsoil, weathered zone and fractured zone are not uniform in the research area. Top soil plus weathered zone acts as a water table (phreatic) aquifer, which extends up to 38 m from the surface. Fractured zone extends up to 45 m, which acts as a kind of confined/semi-confined aquifer. Open and bore wells have been constructed to tap groundwater from the unconfined (water table) and confined/semi-confined aquifers, respectively. High to very high groundwater potential areas are associated with low resistivity, high thickness, low longitudinal conductance, high transverse unit resistance and high aquifer anisotropy areas. Very high groundwater potential areas are mostly confined to flood plain (alluvium) deposits in the central portion of the study area. High potential areas are noticed in the northern part, whereas low potential areas are noticed in the southern part. The areas with high longitudinal conductance indicate low permeable zones with less possibility of external pollution. Since agriculture is an important activity in the study region, this work will be useful to provide water supply for irrigation as well as for domestic needs.

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

The groundwater is very precious in the world. Rapid urbanization and industrialization create tremendous stress on groundwater quality and quantity. Unscientific groundwater extraction and waste disposal methods impact the groundwater aquifer's susceptibility in the coastal area. This research examines how industrial waste, seawater intrusion, and solid waste dumping affect the Thoothukudi District, located on the southwest coast of Tamil Nadu, India. The groundwater vulnerability potential is determined using the DRASTIC and analytical hierarchy process (AHP)-based DRASTIC model. DRASTIC-AHP method's weights and ranks are determined using multi-criteria decision analysis (MCDA)-based pairwise comparison method. Remote sensing (RS) and geographical information system (GIS) are implemented to prepare the input layers for DRASTIC and DRASTIC-AHP. The findings reveal a very high category of vulnerability along the coastline that is covered in sand and loose sediments from an aquifer. Similar conditions exist on the southeast side, which is covered with gravel, sand, and sandstone with shale and has relatively low-slope topography. This enables higher contaminant percolation into the groundwater and raises the possibility for pollution. The DRASTIC-AHP method's results reveal that the southeast side has a significant possibility of contamination. The water table, net recharge, vadose zone, and conductivity greatly impacted the DRASTIC vulnerability assessment due to their stronger weight than theoretical weight. It may be stated that the DRASTIC technique is more cost-effective and time-efficient in analyzing a wide range of regional groundwater risks while avoiding sloppy, uncontrolled land development and other unwanted activities.