عنوان
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Mapping specific vulnerability of multiple confined and unconfined quifers by using artificial intelligence to learn from multiple DRASTIC frameworks
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نوع پژوهش
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مقاله چاپشده در مجلات علمی
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کلیدواژهها
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Groundwater vulnerability, DRASTIC, Fuzzy-catastrophe-DRASTIC, Support vector machine, AI driving multi frameworks (AIMF)
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چکیده
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An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF prescribes rates and weights for 7 data layers but FCF is an unsupervised learning framework based on a multicriteria decision theory by integrating fuzzy membership function and catastrophe theory. The challenges in the paper include: (i) the study area comprises confined and unconfined aquifers and (ii) Artificial Intelligence (AI) is used to run Multiple Framework (AIMF) in order to map specific vulnerability due to a specific contaminant. Predicted results by AIMF are referred to as Specific Vulnerability Indices, as the learned VIs are referenced to site-specific nitrate-N. The results show that correlation coefficient between BDF or FCF with nitrate-N is lower than 0.7 but the AIMF strategy improves it to greater than 0.95. The results are evidence for the proof-of-concept for transforming frameworks to models capable of further learning.
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پژوهشگران
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عطاالله ندیری (نفر اول)، زهرا صدقی (نفر دوم)، رحمان خطیبی (نفر سوم)، سینا صادق فام (نفر چهارم)
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