Publications by authors named "Sina Sadeghfam"

6 Publications

  • Page 1 of 1

Decision-making process of partnership in establishing and managing of rural wastewater treatment plants: Using intentional and geographical-spatial location data.

Water Res 2021 Jun 31;197:117096. Epub 2021 Mar 31.

University of Maragheh, Department of Biosystem Mechanics Engineering, 55181-83111, Iran. Electronic address:

The construction of rural wastewater treatment plants (RWTPs) is an effective initiative to achieve sustainable water resources, especially in the rural areas situated upstream of the dams where water and waste substances produced by villages are discharged into the wastewater behind the dams. Neither is the initiative feasible to be launched without the partnership of local people. For this reason, we gained insights into the determinants of villagers' desire and intention to have a share in the construction and management of RWTPs and determined the best place to locate the RWTPs using Fuzzy Catastrophe Scheme (FCS). The study benefited from the survey of 180 rural people from two villages situated upstream of Alavian dam, Maragheh Township, northwestern Iran. Using the theory of goal-directed behavior (TGDB) and rational choice theory (RCT), we designed a questionnaire, inclusive of the constructs relevant to the hypothetical relationships. The Cronbach's alpha method and discriminant analysis were used to make sure that indicator variables were consistently loaded with pertinent latent variables. The results of PLS-SEM manifested a proper fitted model with the data. As would be hypothesized, attitude towards participation, PBC, positive emotions had a positive impact on desire, which directly influences the intention to participate in the construction and management of RWTPs. To locate RWTPs as geographically and spatially as possible, we made use of suitability index (SI), formulated by the FCS, the resulting evidence demonstrated that the spatial distribution of SI would be classified into five bands using Jenks' optimization method, northwestern areas located in Bands 1 and 2 were appropriate areas, whereas, western and northwestern areas (in Bands 4-5) were caught sight of being not appropriate areas. The implications delivered in conclusion would be useful for the same cases in other parts of the world and further to stimulate rural people to participate in the construction of RWTPs and locate the best area for setting up the plants.
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http://dx.doi.org/10.1016/j.watres.2021.117096DOI Listing
June 2021

An investigation to human health risks from multiple contaminants and multiple origins by introducing 'Total Information Management'.

Environ Sci Pollut Res Int 2021 Apr 21;28(15):18702-18724. Epub 2021 Jan 21.

Department of Geology, V.O. Chidambaram College, Tuticorin-8, Tamil Nadu, India.

A capability for aggregating risks to aquifers is explored in this paper for cases with sparse data exposed to anthropogenic and geogenic contaminants driven by poor/non-existent planning/regulation practices. The capability seeks 'Total Information Management' (TIM) under sparse data by studying hydrogeochemical processes, which is in contrast to Human Health Risk Assessment (HHRA) by the USEPA for using sample data and a procedure with prescribed parameters without deriving their values from site data. The methodology for TIM pools together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations to determine groundwater type, origin, and dominant processes (e.g. statistical, graphical, multivariate analysis and geological survey); (iii) risk cells to contextualise contaminants, where the paper considers nitrate, arsenic, iron and lead occurring more than three times their permissible values; (iv) 'soft modelling' to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins, Sources, Pathways, Receptors and Consequence). The study area comprises a series of patchy aquifers but HHRA ignores such contextual data and provides some evidence on both carcinogenic and non-carcinogenic risks to human health. The TIM capability provides a greater insight for the processes to unacceptable risks from minor ions of anthropogenic nitrate pollutions and from trace ions of arsenic, iron and lead contaminants.
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http://dx.doi.org/10.1007/s11356-020-11853-2DOI Listing
April 2021

Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM).

J Environ Manage 2020 Feb 3;255:109871. Epub 2019 Dec 3.

Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, East Azerbaijan, Iran. Electronic address:

Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for mapping the vulnerability to SWI. This AI-based modeling strategy is a two-level learning process, where vulnerability to SWI at Level 1 can be predicted by such models as Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL), and Neuro-Fuzzy (NF); and their outputs serve as the input to the model at Level 2, such as Support Vector Machine (SVM). This model is applied to Urmia aquifer, west coast of Lake Urmia, where both are currently declining. The construction of the above four models both at Levels 1 and 2 provide tools for mapping the SWI vulnerability of the study area. Model performances in the paper are studied using RMSE and R metrics, where the models at Level 1 are found to be fit-for-purpose and the SVM at Level 2 is improved particularly with respect to the reduced scale of scatters in the results. Evaluating the result and groundwater samples by Piper diagram confirms the correspondence of SWI status with vulnerability index.
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http://dx.doi.org/10.1016/j.jenvman.2019.109871DOI Listing
February 2020

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

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

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