Groundwater management can be effectively conducted by using groundwater contamination map assessment. In this study, a modified DRASTIC approach using geographic information system (GIS) was applied to evaluate groundwater vulnerability in Kerman plain (Iran). The Wilcoxon rank-sum nonparametric statistical test was applied to modify the rates of DRASTIC. In addition, the analytic hierarchy process (AHP) method was employed to evaluate the validity of the criteria and sub criteria of all the parameters of the DRASTIC model, which proposed as an alternative treatment of the imprecision demands. The GIS offers spatial analysis in which the multi index evaluation can be effectively conducted through the AHP. The non-point source pollution was effectively determined by the modified DRASTIC method compared with the traditional method. The regression coefficient revealed the relationship between the vulnerability index and the nitrate concentration. The best result was obtained by using AHP–AHP, followed by DRASTIC–AHP, modified DRASTIC–AHP, and AHP–DRASTIC models. In this study, the DRASTIC method failed to provide satisfactory result. Additionally, by using both the original DRASTIC and the modified DRASTIC methods in the study area, AHP–AHP performed highly in the Kerman plain, suggesting that the southern and south east parts of the area considerably calls for conservation against contamination.

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Groundwater vulnerability assessment using an improved DRASTIC method in GIS

Assessment and mapping of groundwater vulnerability to pollution: Current status and challenges

Factor weighting in DRASTIC modeling.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

The multivariate statistical structure of DRASTIC model

The COVID-19 pandemic has made a significant impact on various supply chains (SCs). All around the world, the COVID-19 pandemic affects different dimensions of SCs, including but not limited to finance, lead time, demand changes, and production performance. There is an urgent need to respond to this grand challenge. The catastrophic impact of the COVID-19 pandemic prompted scholars to develop innovative SC disruption management strategies and disseminate them via numerous scientific articles. However, there is still a lack of systematic literature survey studies that aim to identify promising SC disruption management strategies through the bibliometric, network, and thematic analyses. In order to address this drawback, this study presents a set of up-to-date bibliometric, network, and thematic analyses to identify the influential contributors, main research streams, and disruption management strategies related to the SC performance under the COVID-19 settings. The conducted analyses reveal that resilience and sustainability are the primary SC topics. Furthermore, the major research themes are found to be food, health-related SCs, and technology-aided tools (e.g., artificial intelligence (AI), internet of things (IoT), and blockchains). Various disruption management strategies focusing on resilience and sustainability themes are extracted from the most influential studies that were identified as a part of this work. In addition, we draw some managerial insights to ensure a resilient and sustainable supply of critical products in the event of a pandemic, such as personal protective equipment (PPE) and vaccines. 

https://doi.org/10.1016/j.ijdrr.2022.102983

Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies

Parallel to industrial growth and ever increasing use of agrichemicals, environmental resources have been affected and deteriorated by generated pollutants. Groundwater, an important source of fresh water, has not been immune from contamination. Recognition of groundwater vulnerability to pollution will help in managing groundwater quality conflicts. The DRASTIC model (where D=depth to groundwater; R=net recharge; A=aquifer media; S=soil type; T=topography; I=impact of vadose zone; and C=hydraulic conductivity of the aquifer) has been used extensively for assessing the vulnerability of groundwater. It employs a linear combination of some intrinsic properties of aquifers to develop a vulnerability index. As there is no clear boundary for the set of vulnerable aquifers, groundwater vulnerability can be addressed through fuzzy set theory instead of classical set theory. In this study, benefiting from a fuzzy system and a conscious knowledge base, a regional-scale model is developed for groundwater vulnerability assessment that employs DRASTIC parameters. A comparison between the output of the fuzzy model and the DRASTIC index is accomplished. The ability of the fuzzy system to cope with the modeling of a nonlinear system and presentation of the output of the fuzzy system in the framework of a geographical information system are highlighted.

J. Moosavi

Papers

Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies

Rule-Based Fuzzy System for Assessing Groundwater Vulnerability