Abstract
Hybrid and integrated techniques can be used to investigate intrinsic uncertainties of the overlay and index groundwater vulnerability assessment methods. The development of a robust groundwater vulnerability assessment framework for precise identification of susceptible zones may contribute to more efficient policies and plans for sustainable managements. To achieve an overall view of the groundwater pollution potential, the DRASTIC framework (Depth to the water table, net Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity) can be used for intrinsic vulnerability assessment. However, the unreliability of this index is because of its inherent drawbacks, including the weight and rating assignment subjectivity. To modify the rating range, this study recommended a new DRASTIC modification using a recently introduced Multi-Criteria Decision-Making (MCDM) method, namely the Stepwise Weight Assessment Ratio Analysis (SWARA); in addition, the Entropy and Genetic Algorithm (GA) methods were employed to alter the relative weights of DRASTIC parameters. To improve the DRASTIC index, nitrate concentration data from 50 observation wells in the study site were used. To assess the models’ overall performance, the datasets obtained from new observation wells, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were studied. The experiments were carried out in the aquifer of the Qazvin Plain in Iran. The results indicated the higher performance of the modified DRASTIC framework, manifested as an increase in the AUC value from 0.58 for generic DRASTIC to 0.68 for the SWARA-Ent framework and 0.74 for the SWARA-GA framework. The application of the SWARA technique, as an effective MCDM method, resulted in the DRASTIC rating system enhancement. The generic DRASTIC optimization by integrating SWARA and GA provided an effective framework to assess groundwater vulnerability to nitrate contamination in the Qazvin Plain.
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All authors contributed to the study conception and design. Data collection and material preparation were performed by SJ. Development and design of methodology and creation of models were performed by MT and AN. HY consulted for the methodology application. AN and SJ were the supervisors of the work. The first draft of the manuscript was written by MT, and all authors commented on previous versions of the manuscript.
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Appendix
Appendix
In the present study, DRASTIC index was calibrated using SWARA method as the rate modifier Entropy method as the weight modifier and GA as the weight optimizer. Figure 13 shows the seven DRASTIC layers after SWARA-Entropy modification. The SWARA rates are calculated in range [0,1]. Also, the obtained Entropy weights were presented in a normalized form in range [0,1]. Figure 14 shows the seven DRASTIC layers after SWARA-GA optimization. The optimum weights from GA were calculated in range [1,5] according to generic DRASTIC weighting system. Therefore, the pixel values of SWARA-Entropy layers, calculating by multiplying SWARA rates and Entropy weights, are lower than the pixel values of SWARA-GA layers.
seven layers of DRASTIC index afterSWARA-Entropy modification
seven layers of DRASTIC index afterSWARA-GA optimization
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Torkashvand, M., Neshat, A., Javadi, S. et al. DRASTIC framework improvement using Stepwise Weight Assessment Ratio Analysis (SWARA) and combination of Genetic Algorithm and Entropy. Environ Sci Pollut Res 28, 46704–46724 (2021). https://doi.org/10.1007/s11356-020-11406-7
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DOI: https://doi.org/10.1007/s11356-020-11406-7