Publications by authors named "Maryam Gharekhani"

5 Publications

  • Page 1 of 1

Introducing the risk aggregation problem to aquifers exposed to impacts of anthropogenic and geogenic origins on a modular basis using 'risk cells'.

J Environ Manage 2018 Jul 10;217:654-667. Epub 2018 Apr 10.

Department of Geology, Faculty of Sciences, University of Urmia, Urmia, West Azerbaijan, Iran. Electronic address:

Proof-of-concept is presented in this paper to a methodology formulated for indexing risks to groundwater aquifers exposed to impacts of diffuse contaminations from anthropogenic and geogenic origins. The methodology is for mapping/indexing, which refers to relative values but not their absolute values. The innovations include: (i) making use of the Origins-Source-Pathways-Receptors-Consequences (OSPRC) framework; and (ii) dividing a study area into modular Risk (OSPRC) Cells to capture their idiosyncrasies with different origins. Field measurements are often sparse and comprise pollutants and water table, which are often costly; whereas supplementary data are general-purpose data, which are widely available. Risk mapping for each OSPRC cell is processed by dividing a study area into pixels and for each pixel, the risk from both anthropogenic and geogenic origins are indexed by using algorithms related to: (i) Vulnerability Indices (VI), which identify the potential for risk exposures at each pixel; and (ii) velocity gradient, which expresses the potency to risk exposures across the risk cell. The paper uses DRASTIC for anthropogenic VI but introduces a new framework for geogenic VI. The methodology has a generic architecture and is flexible to modularise risks involving any idiosyncrasies in a generic way in any site exposed to environmental pollution risks. Its application to a real study area provides evidence for the proof-of-concept for the methodology by a set of results that are fit-for-purpose and provides an insight into the study area together with the identification of its hotspots.
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http://dx.doi.org/10.1016/j.jenvman.2018.04.011DOI Listing
July 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

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

Environ Sci Pollut Res Int 2017 Mar 13;24(9):8562-8577. Epub 2017 Feb 13.

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

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.
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http://dx.doi.org/10.1007/s11356-017-8489-4DOI Listing
March 2017

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

Sci Total Environ 2017 Jan 14;574:691-706. Epub 2016 Oct 14.

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

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters.
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http://dx.doi.org/10.1016/j.scitotenv.2016.09.093DOI Listing
January 2017

Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran.

J Environ Health Sci Eng 2016 9;14:13. Epub 2016 Aug 9.

Research Engineer at EnTech Engineering, PC11 broadway 21st floor, New York, NY 10004 USA.

Background: Extensive human activities and unplanned land uses have put groundwater resources of Shiraz plain at a high risk of nitrate pollution, causing several environmental and human health issues. To address these issues, water resources managers utilize groundwater vulnerability assessment and determination of protection. This study aimed to prepare the vulnerability maps of Shiraz aquifer by using Composite DRASTIC index, Nitrate Vulnerability index, and artificial neural network and also to compare their efficiency.

Methods: The parameters of the indexes that were employed in this study are: depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity, and land use. These parameters were rated, weighted, and integrated using GIS, and then, used to develop the risk maps of Shiraz aquifer.

Results: The results indicated that the southeastern part of the aquifer was at the highest potential risk. Given the distribution of groundwater nitrate concentrations from the wells in the underlying aquifer, the artificial neural network model offered greater accuracy compared to the other two indexes. The study concluded that the artificial neural network model is an effective model to improve the DRASTIC index and provides a confident estimate of the pollution risk.

Conclusions: As intensive agricultural activities are the dominant land use and water table is shallow in the vulnerable zones, optimized irrigation techniques and a lower rate of fertilizers are suggested. The findings of our study could be used as a scientific basis in future for sustainable groundwater management in Shiraz plain.
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http://dx.doi.org/10.1186/s40201-016-0254-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977699PMC
August 2016