Landfilling is the least preferred method in the hierarchy of solid waste management options, but it is the most widely practiced option. Thus, identification of environmentally and economically suitable landfill sites should be of prime importance. The main objective of this study is to identify environmentally and economically suitable landfill sites using fuzzy analytical hierarchy process-based weighted linear combination model within a GIS environment. This study also used the DRASTIC-based groundwater vulnerability index and distance of landfills from densely populated areas to protect groundwater and reduce cost of transportation of solid waste which were not considered by the previous studies. Using the previously reported methods, a total of 132 landfill sites were found environmentally suitable in the study area. But, after applying DRASTIC-based groundwater vulnerability index, the number of environmentally suitable sites reduced to 95. When the proximity of the 95 sites to densely populated areas was considered to reduce waste transportation cost, the number of selected sites further reduced to 21 site and they can be considered the most environmentally and economically suitable landfill sites. This study will help the policy makers and the concerned SWM authorities to construct the engineered landfills at environmentally and economically suitable landfill sites in the study area and in other similar areas.

Groundwater vulnerability assessment has nowadays evolved into an essential tool towards proper groundwater protection and management, while the DRASTIC method is included among the most widely applied vulnerability assessment methods. However, the high uncertainty of the DRASTIC method mainly associated with the subjectivity in assigning parameters ratings and weights has driven many researchers to apply various methods for improving its efficiency. In this context, in the present study, different techniques were implemented with the aim of modifying the DRASTIC framework and thus enhancing its performance for groundwater vulnerability assessment in the Bouficha aquifer, Tunisia. In a first stage, the land use type (L) was incorporated as an additional parameter in the typical DRASTIC framework, thus taking into consideration the impact of anthropogenic activities on groundwater vulnerability. Subsequently, the rating and weighting systems of the developed DRASTIC-L framework were modified through the application of statistical methods (DRASTIC-L-SA) and genetic algorithms (GA) (DRASTIC-L-GA) in an attempt to investigate and compare both linear and nonlinear modifications. To evaluate the various vulnerability frameworks, correlation between vulnerability values and nitrate concentrations, expressed as Spearman\'s rank correlation coefficient (ρ) and Correlation Index (CI), was examined. The results revealed that the DRASTIC-L-GA framework developed by applying a fully GA-based optimization procedure provided the highest values in terms of the performance metrics used, making it the most suitable for the study area. In addition, the aquifer under study was found to be less vulnerable to pollution when employing the typical DRASTIC framework instead of the modified ones, leading to the conclusion that the former substantially underestimates pollution potential in the study area.

A vulnerability assessment of the aquifers in the agricultural area of Zacharo in SW, Peloponnese, Greece, was conducted using the DRASTIC index and the susceptibility index (SI). Sensitivity analysis was conducted and thematic maps for each parameter were generated to analyse the impact of individual parameter on the collective groundwater vulnerability. Results derived from the DRASTIC and SI maps revealed that the extremely highly vulnerable zones are concentrated at three coastal sites in the western part of the study area. Data from these maps also indicate low vulnerability areas throughout the eastern part of the region. The distribution of nitrate concentrations in groundwater is better correlated with the DRASTIC (79.2%) compared to SI (60.2%). Neither method takes into consideration the impact of dilution and nitrate to ammonium reduction, on the nitrate content of groundwater, thus overestimating the vulnerability index. Moreover, the SI method overestimates the impact of olive groves\' land use type on the susceptibility index, thus resulting to a lower correlation with the observed nitrate concentrations.

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.

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.