Groundwater vulnerability to pollution mapping of Ranchi district using GIS

Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district

Most of the groundwater vulnerability assessment methods using machine learning are binary classification. This study attempts multi-class classification models to map the groundwater vulnerability against Nitrate contamination. Further, the significance of the number of classes used in the multi-class classification is studied by considering three and five classes. Three machine learning models, namely Random Forest, Extreme Gradient Boosting and CART, with two classification schemes, were developed for the present study. The parameters used in the conventional DRASTIC method and with an additional parameter, Landuse, have been employed for the study. Evaluation metrics such as Accuracy, Kappa, Positive Predictive Value, Negative Predictive Value, and Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) were compared among all six models to select the optimal one. Based on the model evaluation metrics and consistent distribution of area among the classes Random Forest model with a three-class classification with an AUC of 0.95 is considered optimum for the selected objective. This study highlights the importance of the data classification process and the selection of the number of classes for ML model prediction in assessing groundwater vulnerability. Leveraging the effectiveness of the Geographic Information system and advanced machine learning techniques, the proposed approach offers valuable insights for enhanced groundwater management and contamination mitigation strategies.

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

Totko river basin is a semi-arid watershed, which undergoes severe water crisis during the dry season. Presently, due to increase in population, demand of food has increased leading to a rise in growth of high yield crop variety and usage of chemical fertilizers and pesticides. So, surface water as well as groundwater is getting polluted. In this study assessment of groundwater vulnerability of Totko river basin has been done using DRASTIC and Analytic Hierarchy Process (AHP) models. For this, seven hydrogeological parameters have been considered which are; Depth to water level (D), Net Recharge (R), Aquifer media (A), Soil media (S), Topography (T), Impact of vadose zone (I) and Hydraulic Conductivity (C). Weight and rating analysis of the seven criteria and their sub-criteria have been done using generic DRASTIC algorithm and AHP comparison matrix. Ground Water Vulnerability Map (GWV) obtained from DRASTIC and AHP analysis has been divided into five vulnerable classes. Area of very high vulnerable zone is 6.53% more in AHP based vulnerability as compared to Generic DRASTIC. Similarly, these regions show a high nitrate concentration (30-50 ppm) in groundwater. GWV maps have been validated through nitrate concentration and the accuracy of the models have been assessed through Pearson's correlation coefficient and Kappa coefficient. To prevent groundwater contamination proper land use planning and watershed management are necessary, for which vulnerable zones need to be demarcated and DRASTIC is a useful model for vulnerability assessment.

Assessment of groundwater vulnerability in an urban area: a comparative study based on DRASTIC, EBF, and LR models

The groundwater vulnerability assessment is known as a useful tool for predicting and prevention of groundwater pollution. This study targets the DRASTIC, evidential belief function (EBF), and logistic regression (LR) models to assess vulnerability in Kabul aquifers, Afghanistan Country. The growth of urban sprawl, groundwater overexploitation, and lack of suitable municipal sewage systems as anthropogenic sources have been the main potential to increase groundwater contaminants such as nitrate in the study area. The vulnerability map has been developed based on various effective factors including altitude, slope (percentage rise), aspect, curvature, land-use type, drainage density, distance from river, annual mean precipitation, net recharge, geology/lithology units, the impact of the vadose zone, aquifer media, depth to water (unsaturated zone), saturated zone, drawdown, and hydraulic conductivity. To identify groundwater pollution, the spatial variation of nitrate concentration data in 2018 was considered indication of groundwater pollution. Based on descriptive statistics, the value of 2.65 mg/l (the median of the pixel values of nitrate map) was selected as a threshold to differentiate the occurrence and non-occurrence of pollution. The groundwater quality data were selected and randomly divided into two datasets for training and validation, including 70% and 30%, respectively. The success-rate and prediction-rate curves were computed based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to estimate the efficiency of models. The ROC-AUC of success rates for EBF, LR, and DRASTIC models were estimated to be 67%, 66%, and 52%, respectively. Moreover, the ROC-AUC of the prediction rates of the EBF, LR, and DRASTIC models were obtained 61%, 63%, and 55%, respectively. Based on correlation between mean nitrate concentration and the mean vulnerability indexes in each model, the EBF model is the most compatible with the current developed vulnerability zones as the role of mankind in changing the environment in real conditions in comparison to LR and DRASTIC models.

Modified Index-Overlay Method to Assess Spatial–Temporal Variations of Groundwater Vulnerability and Groundwater Contamination Risk in Areas with Variable Activities of Agriculture Developments

The groundwater vulnerability (GV) assessment for contamination is an effective technique for the planning, policy, and decision-making, as well as for sustainable groundwater resource protection and management. The GV depends strongly on local hydrogeological settings and land-use conditions that may vary in response to the activities of agricultural development. In this study, a modified DRASTIC model, which employs an additional factor of land use coupled with the analytic hierarchy process (AHP) theory, was used to quantify the spatial and temporal variation of GV and groundwater contamination risk in the Pingtung groundwater basin. The results show that the GV slightly decreased due to the decrease in agricultural areas under the change of land use over two decades (1995–2017). The yearly changes or a shorter period of observations incorporated with the accurate land-use map in DRASTIC parameters could improve GV maps to obtain a better representation of site-specific conditions. Meanwhile, the maps of yearly contamination risk indicated that the counties of Jiuru and Ligang are at high risk of nitrate pollution since 2016. In other agriculture-dominated regions such as Yanpu, Changzhi, and Gaoshu in the Pingtung groundwater basin, the climate conditions influence less the temporal variations of groundwater contamination risk. The results of this study are expected to support policy-makers to adopt the strategies of sustainable development for groundwater resources in local areas.

Assessment of groundwater vulnerability by applying the modified DRASTIC model in Beihai City, China

This study assesses vulnerability of groundwater to pollution in Beihai City, China, as a support of groundwater resource protection. The assessment result not only objectively reflects potential possibility of groundwater to contamination but also provides scientific basis for the planning and utilization of groundwater resources. This study optimizes the parameters consisting of natural factors and human factors upon the DRASTIC model and modifies the ratings of these parameters, based on the local environmental conditions for the study area. And a weight of each parameter is assigned by the analytic hierarchy process (AHP) to reduce the subjectivity of humans to vulnerability assessment. The resulting scientific ratings and weights of modified DRASTIC model (AHP-DRASTLE model) contribute to obtain the more realistic assessment of vulnerability of groundwater to contaminant. The comparison analysis validates the accuracy and rationality of the AHP-DRASTLE model and shows it suits the particularity of the study area. The new assessment method (AHP-DRASTLE model) can provide a guide for other scholars to assess the vulnerability of groundwater to contamination. The final vulnerability map for the AHP-DRASTLE model shows four classes: highest (2%), high (29%), low (55%), and lowest (14%). The vulnerability map serves as a guide for decision makers on groundwater resource protection and land use planning at the regional scale and that it is adapted to a specific area.

Effects of open dumping of MSW on metal contamination of soil, plants, and earthworms in Ranchi, Jharkhand, India

Influence of open dumping of municipal solid wastes (MSW) on metal contamination of soil, plants, and earthworms in Ranchi, Jharkhand, India, was studied over 6-month period. Dumpsite in the study area exists in two sections, old section where waste dumping has stopped and new section where wastes are currently disposed. Soil around dumpsite had high concentration of Co, Cr, Cu, Pb, and Zn than that at control site. Geoaccumulation index indicated uncontaminated to moderate level of soil contamination at old dumpsite and soil at new dumpsite was found to be uncontaminated. Parthenium hysterophorus, Lantana camara, and Calotropis procera were the main plants found in patchy distribution around dumpsite. Plants exhibited almost similar levels of metal concentration in roots and shoots. P. hysterophorus and L. camara showed high bioaccumulation capacity and low translocation capacity. C. procera showed moderate bioaccumulation capacity and high translocation capacity as the concentration of metals was higher in the shoot. P. hysterophorus and L. camara due to higher bioaccumulation capacity and lower translocation capacity appear to be suitable for phytostabilization of metal-contaminated soil. Earthworms present at the dumpsite showed high concentration of Cr, Cu, Pb, and Zn with bioconcentration factor > 1. Results highlights that soil contamination due to metals is occurring at the dumpsite which is also leading to transfer of metals to plants and earthworms which can pose serious risk to environment and human health. The plants identified can be used for decontamination of metals from the dumpsite.

Multi-criteria evaluation of hydro-geological and anthropogenic parameters for the groundwater vulnerability assessment

Groundwater contamination assessment is a challenging task due to inherent complex dynamisms associated with the groundwater. DRASTIC is a very widely used rapid regional tool for the assessment of vulnerability of groundwater to contamination. DRASTIC has many lacunas in the form of subjectivities associated with weights and ratings of its hydro-geological parameters, and, therefore, the accuracy of the DRASTIC-based vulnerability map is questioned. The present study demonstrates the optimisation of the DRASTIC parameters along with a scientific consideration to the anthropogenic factors causing groundwater contamination. The resulting scientific consistent weights and ratings to DRASTIC parameters assist in the development of a very precise groundwater vulnerability map highlighting different zones of different gravity of contamination. One of the most important aspects of this study is that we have considered the impact of vadose zone in a very comprehensive manner by considering every sub-surface layer from the earth surface to the occurrence of groundwater. The study area for our experiment is Fatehgarh Sahib district of Punjab which is facing several groundwater issues.

Evaluation of index-overlay methods for groundwater vulnerability and risk assessment in Kathmandu Valley, Nepal

This study aimed at evaluating three index-overlay methods of vulnerability assessment (i.e., DRASTIC, GOD, and SI) for estimating risk to pollution of shallow groundwater aquifer in the Kathmandu Valley, Nepal. The Groundwater Risk Assessment Model (GRAM) model was used to compute the risk to groundwater pollution. Results showed that DRASTIC and SI methods are comparable for vulnerability assessment as both methods delineate around 80% of the groundwater basin area under high vulnerable zone. From the perspective of risk to pollution results, DRASTIC and GOD methods are comparable. Nevertheless, all the three methods estimate that at least 60% of the groundwater basin is under moderate risk to NO₃-N pollution, which goes up to 75% if DRASTIC or GOD-based vulnerabilities are considered as exposure pathways. Finally, based on strength and significance of correlation between the estimated risk and observed NO₃-N concentrations, it was found that SI method is a better-suited one to assess the vulnerability and risk to groundwater pollution in the study area. Findings from this study are useful to design strategies and actions aimed to prevent nitrate pollution in groundwater of Kathmandu Valley in Nepal.