عنوان
|
Mapping specific vulnerability of multiple confined and unconfined quifers by using artificial intelligence to learn from multiple DRASTIC frameworks
|
نوع پژوهش
|
مقاله چاپشده در مجلات علمی
|
کلیدواژهها
|
Groundwater vulnerability, DRASTIC, Fuzzy-catastrophe-DRASTIC, Support vector machine, AI driving multi frameworks (AIMF)
|
چکیده
|
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.
|
پژوهشگران
|
سینا صادق فام (نفر چهارم)، رحمان خطیبی (نفر سوم)، زهرا صدقی (نفر دوم)، عطاالله ندیری (نفر اول)
|