Publications by authors named "Amir Fadaei Nobandegani"

3 Publications

  • Page 1 of 1

A computer-based approach for data analyzing in hospital's health-care waste management sector by developing an index using consensus-based fuzzy multi-criteria group decision-making models.

Int J Med Inform 2018 10 11;118:5-15. Epub 2018 Jul 11.

Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Razi St, Shiraz, Iran. Electronic address:

Background: Proper Health-Care Waste Management (HCWM) and integrated documentation in this sector of hospitals require analyzing massive data collected by hospital's health experts. This study presented a quantitative software-based index to assess the HCWM process performance by integrating ontology-based Multi-Criteria Group Decision-Making techniques and fuzzy modeling that were coupled with data mining. This framework represented the Complex Event Processing (CEP) and Corporate Performance Management (CPM) types of Process Mining in which a user-friendly software namely Group Fuzzy Decision-Making (GFDM) was employed for index calculation.

Findings: Assessing the governmental hospitals of Shiraz, Iran in 2016 showed that the proposed index was able to determine the waste management condition and clarify the blind spots of HCWM in the hospitals. The index values under 50 were found in some of the hospitals showing poor process performance that should be at the priority of optimization and improvement.

Conclusion: The proposed framework has distinctive features such as modeling the uncertainties (risks) in hospitals' process assessment and flexibility enabling users to define the intended criteria, stakeholders, and number of hospitals. Having computer-aided approach for decision process also accelerates the index calculation as well as its accuracy which would contribute to more willingness of hospitals' experts and other end-users to use the index in practice. The methodology could efficiently be employed as a tool for managing hospitals' event logs and digital documentation in big data environment not only for the health-care waste management, but also in other administrative wards of hospitals.
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October 2018

Groundwater vulnerability assessment in karstic aquifers using COP method.

Environ Sci Pollut Res Int 2018 Jul 2;25(19):18960-18979. Epub 2018 May 2.

Environmental Science and Technology Research Center, Department of Environmental Health Engineering, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

Access to safe and reliable drinking water is amongst the important indicators of development in each society, and water scarcity is one of the challenges and limitations affecting development at national and regional levels and social life and economic activity areas. Generally, there are two types of drinking water sources: the first type is surface waters, including lakes, rivers, and streams and the second type is groundwaters existing in aquifers. Amongst aquifers, karst aquifers play an important role in supplying water sources of the world. Therefore, protecting these aquifers from pollution sources is of paramount importance. COP method is amongst the methods to investigate the intrinsic vulnerability of this type of aquifers, so that areas susceptible to contamination can be determined before being contaminated and these sources can be protected. In the present study, COP method was employed in order to spot the regions that are prone to contamination in the region. This method uses the properties of overlying geological layers above the water table (O factor), the concentration of flow (C factor), and precipitation (P factor) over the aquifer, as the parameters to assess the intrinsic vulnerability of groundwater resources. In this regard, geographical information system (GIS) and remote sensing (RS) were utilized to prepare the mentioned factors and the intrinsic vulnerability map was obtained. The results of COP method indicated that the northwest and the west of the region are highly and very vulnerable. This study indicated that regions with low vulnerability were observed in eastern areas, which accounted for 15.6% of the area. Moderate vulnerability was 40% and related to the northeast and southeast of the area. High vulnerability was 38.2% and related to western and southwestern regions. Very high vulnerability was 6.2% and related to the northwest of the area. By means of the analysis of sensitivity of the model, it was determined that the focus factor of the flow has the greatest impact on the creation of vulnerability in the region. Also, these results were validated through electrical conductivity and discharge time series of the regional springs that are located in the vulnerable zones.
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July 2018

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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August 2016