Publications by authors named "Zahra Sedghi"

2 Publications

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Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks.

J Environ Manage 2018 Dec 13;227:415-428. Epub 2018 Aug 13.

Department of Civil Engineering, Faculty of Engineering, University of Maragheh, P.O. Box 55136-553, Maragheh, East Azerbaijan, Iran. Electronic address:

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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http://dx.doi.org/10.1016/j.jenvman.2018.08.019DOI Listing
December 2018

Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures.

Sci Total Environ 2017 Sep 22;593-594:75-90. Epub 2017 Mar 22.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran. Electronic address:

Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran.
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http://dx.doi.org/10.1016/j.scitotenv.2017.03.109DOI Listing
September 2017