Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN

Boloorani, Ali Darvishi; Shorabeh, Saman Nadizadeh; Samany‬, Najmeh Neysani; Mousivand, Alijafar; Kazemi, Yasin; Jaafarzadeh, Neemat; Zahedi, Amir; Rabiei, Javad

doi:10.1016/j.envpol.2021.116859

Cited by 46 publications

(24 citation statements)

References 89 publications

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“…Then all the criteria maps are obtained using spatial analysis and normalized based on Eq. 1 ( Neysani Samany et al, 2014 , Boloorani et al, 2021 ).

…”

Section: Methodsmentioning

confidence: 99%

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

et al. 2021

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Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients’ financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients’ transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

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“…Then all the criteria maps are obtained using spatial analysis and normalized based on Eq. 1 ( Neysani Samany et al, 2014 , Boloorani et al, 2021 ).

…”

Section: Methodsmentioning

confidence: 99%

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

et al. 2021

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“…The GIS‐based MCDA requires that the criterion map layers be standardized and converted to comparable units. Based on whether the criterion is to be minimized (i.e., a lower value represents greater migration) or maximized (i.e., a higher value represents greater migration), we used the following equations to standardize the criteria between 0 and 1 (Boloorani et al., 2021; Karimi Firozjaei, Sedighi, & Jelokhani‐Niaraki, 2020; Kiavarz & Jelokhani‐Niaraki, 2017):

a {max}_{italicij} = \frac{S_{italicij} ‐ S_{j}^{min}}{S_{j}^{max} ‐ S_{j}^{min}},

a {min}_{ij} = \frac{S_{j}^{max} ‐ S_{ij}}{S_{j}^{max} ‐ S_{j}^{min}}

where

S_{ij}

is the value for the i th position of the j th criterion,

S_{j}^{min}

and

S_{j}^{max}

denote the minimum and maximum values of the j th criterion respectively, and

a_{ij}

represents the normalized value for the i th position of the j th criterion.…”

Section: Methodsmentioning

confidence: 99%

“…The GISbased MCDA requires that the criterion map layers be standardized and converted to comparable units. Based on whether the criterion is to be minimized (i.e., a lower value represents greater migration) or maximized (i.e., a higher value represents greater migration), we used the following equations to standardize the criteria between 0 and 1 (Boloorani et al, 2021;Karimi Firozjaei, Sedighi, & Jelokhani-Niaraki, 2020;Kiavarz & Jelokhani-Niaraki, 2017):…”

Section: Standardization Of Criteriamentioning

confidence: 99%

Spatial modeling of migration using GIS‐based multi‐criteria decision analysis: A case study of Iran

Mijani

Sani

Dastaran

et al. 2021

Transactions in GIS

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Spatial modeling of migration and the identification of the effective parameters are imperative for planning and managing demographic, economic, social, and environmental changes on various geographical scales. The recent climate change stressors as well as inequality in terms of education and life quality have triggered internal mass migrations in Iran, causing pressure on housing, the job market, and potential slums around large cities. This study proposes a new approach to modeling migration patterns in Iran based on multi‐criteria decision analysis. For this purpose, a total of 23 individual criteria embedded within four criteria groups (economic, socio‐cultural, welfare, and environmental) affecting national migration were used. The analytic hierarchy process was employed to determine weights for the input factors and the weighted linear combination (WLC) model was used for the integration of criteria, based on which maps of migration potential were produced. The model applied was evaluated based on the correlation coefficient between migration potential values obtained from the WLC model and the actual net migration rate. Among the input individual criteria, unemployment, higher education centers, number of physicians, and dust storms were found to influence national migration. Furthermore, our findings reveal that the potential for migration across Iranian provinces is heterogeneous, with the spatial potential for emigration being the highest and lowest in the border and central provinces, respectively. The correlation coefficient calculated between outputs from the WLC model and the net migration rate from 2011 to 2016, was .81, indicating the relatively high performance of the proposed model in producing a migration spatial potential map. Our proposed approach, along with the results achieved, can be useful to decision‐makers and planners in designing data‐driven policies against inequality‐ and climate‐induced stressors.

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“…Часто геоинформационные системы используются в области охраны природы и при оценке воздействия опасных гидрометеорологических явлений на жизнь людей. Средствами ГИС могут быть построены цифровые модели уязвимостей городских кварталов к пыльным и песчаным бурям [55], восприимчивости регионов к оползням [115], оценка ущерба на прибрежных территориях вследствие штормов [113] и другие.…”

Section: Fig 1 Technogenesis Of Oil Fieldsunclassified

Development of geoinformation systems for environmental management and environmental safety in the areas of exploited oil deposits

Buzmakov¹,

Sannikov²,

Sivkov³

et al. 2021

ATN

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The state of environmental protection of natural environment from negative processes has become an integral part of sustainable mining. The analytical review shows the ways of geoinformation system’s preparing a to ensure environmental safety during oil mining process on protected areas. Obtainment information for geoinformation database is based on technologies of aerial remote sensing, experimental modeling of biotic reactions to the impact of technogenic factors by biotesting methods. The geoinformation system should perform observations, assessments, search and regulatory forecasts, based on digital technologies, and develop individual measures for the preservation and restoration of the natural environment. Environmental stresses are usually caused by halogenesis, bitumization, air pollution and mechanogenesis. The use of geoinformation systems to collect information about the state of the natural environment is an essential feature of sustainable mining. Multispectral and panchromatic aerial photography by unmanned aerial vehicle has been successfully used to assess pollution, land degradation, and the effectiveness of land restoration. It is important to develop an unmanned aerial sensing technique for areas, where oil fields and protected are located. Biotesting of the consequences of technogenic transformation of ecosystem components is significantly related to the choice of optimal test objects for conducting experiments. Soil contamination with oil and its processed products affects the condition of all components: plants, microorganisms. It is advisable to develop a methodology for determining the patterns and levels of impact of residual oil and technogenic brines on local natural objects, local soil and water ecosystems based on the results of biotesting. Development of regulations for standard measures for remediation of ecosystems at different levels of pollution, during bitumization, halogenesis of land and water objects. The review shows that there are technological possibilities for creating GIS for monitoring the quality of the natural environment and environmental safety in the areas of oil fields. Digital maps of soils, watersheds, natural and technical systems, the development of technogenic processes and measures to restore the natural environment, allow to provide spatial modeling of natural and technogenic processes. Environmental quality management during oil mining process remains an important goal of geoecological researches.

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Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN

Cited by 46 publications

References 89 publications

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

Spatial modeling of migration using GIS‐based multi‐criteria decision analysis: A case study of Iran

Development of geoinformation systems for environmental management and environmental safety in the areas of exploited oil deposits

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