Evaluation of the Resilience of District 20 of Tehran Metropolitan Region (TMR) against Environmental Hazards Using Fuzzy Functions in GIS Software

Extended abstractIntroduction:The idea of ​​resilience of different social, economic, physical, and managerial orientations has entered urban and regional studies on a large scale. This resilient system can absorb temporary or permanent crises and adapt to rapidly changing conditions without losing its function. Among these, resilience against natural disasters can be explained by how social, economic, institutional, political, and executive capacities of societies affect the increase of resilience and understanding of its dimensions in the society. Environmental crises, such as earthquakes, floods, fires, and climate pollution, have caused environmental vulnerability in cities and consequently created threats to their securities, especially in District 20 of Tehran City. By recognizing the dimensions of vulnerability in District 20 of this city against environmental crises, management strategies can be developed to reduce vulnerability and risks and enhance resilience. For this reason, the main purpose of this study was to evaluate resilience of the neighborhoods in District 20 of Tehran City against environmental crises. To achieve this goal, the Fuzzy Multi-Criteria Decision Model (FMCDM) and K-mean method of classification were used. Methodology:To identify and assess the resilience of District 20 of Tehran against environmental crises, a database was created based on the crises and its spatial information was prepared in 4 criteria and 26 sub-criteria. After creating the spatial database of the mentioned district and compiling the criteria and sub-criteria, a layer of information was prepared in ArcGIS software and a distance map was drawn for each sub-criterion through Euclidean distance mapping in order to measure and manage the resilience. Then, fuzzy operators were applied to draw each fuzzy map (subscale) with a value between 0 and 1. Analytic Network Process (ANP) method was utilized to weight and evaluate the research criteria and sub-criteria. Next, the map of each criterion and sub-criterion was drawn by combining the Euclidean distance and fuzzy operators multiplied by their fuzzy weights obtained from the ANP model in ArcGIS software. Thus, the final map was prepared for each criterion and sub-criterion, which showed their values of resilience to the environmental crises. Then, fuzzy superimposing operators were applied to superimpose the fuzzy weighting maps and a superimposed map of 26 sub-criteria (4 criteria) was obtained for each fuzzy operator. To identify the best fuzzy operator by superimposing the research sub-criteria, analysis of spatial relationships between the independent variables and the dependent variable was done through the Ordinary Least Squares (OLS) regression. Finally, the classical K-mean clustering method was employed to classify the neighborhoods from the perspective of resilience to environmental crises. Discussion:The results showed that the weights and values of the socio-economic criteria, road infrastructure, land use and accessibility in resilience measures were 0.49, 0.23, 0.16, and 0.11, respectively. In the socio-economic, road infrastructure, land use, and accessibility criteria, the sub-criteria of house strength, pedestrian bridge, access to social places, and access to medical centers with the weights of 0.33, 0.43, 0.32, and 0.29 had the highest values in resilience. Among the fuzzy superposition operators, the algebraic addition operator (SUM) had the highest correlation with the research criteria in identifying the resilience of the neighborhoods. The northeast and southeast neighborhoods, as well as the central neighborhoods of District 20 of Tehran, were the most resilient neighborhoods to environmental crises. In the final step of the current research, the classical K-mean method was used to cluster the existing neighborhoods in District 20 of Tehran City based on their resilience to environmental crises. The results revealed that the neighborhoods were divided into 3 clusters. In the first cluster showing a lot of patience, the neighborhoods of Javanmard Qassab, Mansouria and Mangal, Hamzehabad, Sartakht, Ibn Babavieh and Zahirabad, Taghiabad, and Abbasabad were located. In the second cluster indicating moderate tolerance, Dolatabad and Shahadat, Sadeghieh, Shahid Ghayuri, Deilman, Aqdasiyeh, Estakhr, and Alain neighborhoods were situated. Finally, the neighborhoods of Sizdeh Aban, Shahid Beheshti, Firoozabadi, Valiabad, and Hashemabad were located in the third cluster with poor productivity. Conclusion:Environmental crises, such as earthquake, flood, drought, air and water pollution, and fire, have the potential to become harmful in areas where there are no crisis management and risk mitigation. In the 21st century, the world has been hit by such environmental crises as Asian tsunamis, Hurricanes Katrina and Rita, successive earthquakes, flash floods, desert dust storms, and widespread fires. Although predictive tools are able to predict some disasters, future crises cannot be forecast based on empirical evidence. Therefore, increasing the ability of a system called resilience is very important for responding to such crises; yet, its resilience must first be measured. In the present study, the resilience of District 20 of Tehran City to environmental crises was evaluated based on socio-economic, road infrastructure, land use, and accessibility criteria. The results of this modeling led to the extraction of 3 clusters for the resilience of the neighborhoods of District 20 of Tehran against environmental crises. The neighborhoods in the west region had the highest resilience compared to the urban areas. Keywords: resilience, home strength, Analytic Network Process (ANP), fuzzy operator, regression References- Asadzadeh, A., Kötter, T., & Zebardast, E. (2015). An augmented approach for measurement of disaster resilience using connective factor analysis and analytic network process (F’ANP) model. International Journal of Disaster Risk Reduction, 14, 504-518.- Bacud, S. T. (2018). Integration of Indigenous and Scientific Knowledge in Disaster Risk Reduction: Resilience Building of a Marginalized Sampaguita Growing Community in the Philippines. Procedia engineering, 212, 511-518.‏- Borsekova, K., Nijkamp, P., & Guevara, P. (2018). Urban resilience patterns after an external shock: An exploratory study. International journal of disaster risk reduction, 31, 381-392.- Caschili, S., Reggiani, A., & Medda, F. (2015). Resilience and vulnerability of spatial economic networks. Networks and Spatial Economics, 15(2), 205-210.- Chen, C., Xu, L., Zhao, D., Xu, T., & Lei, P. (2020). A new model for describing the urban resilience considering adaptability, resistance and recovery. Safety science, 128, 104756.‏- Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global environmental change, 18(4), 598-606.‏- Davis, I., & Izadkhah, Y. O. (2006). Building resilient urban communities. Open House International, 31(1), 11-21.- Fakhruddin, B. S., Reinen-Hamill, R., & Robertson, R. (2019). Extent and evaluation of vulnerability for disaster risk reduction of urban Nuku'alofa, Tonga. Progress in Disaster Science, 2, 100017.- Govindarajulu, D. (2020). Strengthening institutional and financial mechanisms for building urban resilience in India. International Journal of Disaster Risk Reduction, 101549.- Harpin, S. B. (2019). Adverse childhood experiences and resilience: implications for marginalized and vulnerable young people. Journal of Adolescent Health, 64(1), 3-4.‏- Kabir, M. H., Sato, M., Habbiba, U., & Yousuf, T. B. (2018). Assessment of Urban Disaster Resilience in Dhaka North City Corporation (DNCC), Bangladesh. Procedia engineering, 212, 1107-1114.- Landry, F., Dupras, J., & Messier, C. (2020). Convergence of urban forest and socio-economic indicators of resilience: A study of environmental inequality in four major cities in eastern Canada. Landscape and Urban Planning, 202, 103856.‏- Moghadas, M., Asadzadeh, A., Vafeidis, A., Fekete, A., & Kötter, T. (2019). A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. International journal of disaster risk reduction, 35, 101069.- Mullick, M. R. A., Tanim, A. H., & Islam, S. S. (2019). Coastal vulnerability analysis of Bangladesh coast using fuzzy logic based geospatial techniques. Ocean & Coastal Management, 174, 154-169.‏- Ran, J., MacGillivray, B. H., Gong, Y., & Hales, T. C. (2019). The application of frameworks for measuring social vulnerability and resilience to geophysical hazards within developing countries: A systematic review and narrative synthesis. Science of the total environment, 134486.- Suárez, M., Gómez-Baggethun, E., Benayas, J., & Tilbury, D. (2016). Towards an urban resilience Index: a case study in 50 Spanish cities. Sustainability, 8(8), 774.- Wills, G., & Hofmeyr, H. (2019). Academic resilience in challenging contexts: Evidence from township and rural primary schools in South Africa. International Journal of Educational Research, 98, 192-205.‏- Zhang, W., Su, S., Wang, B., Hong, Q., & Sun, L. (2020). Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds. Neurocomputing, 413, 487-498.‏- Zhang, X., Song, J., Peng, J., & Wu, J. (2019). Landslides-oriented urban disaster resilience assessment—a case study in ShenZhen, China. Science of the Total Environment, 661, 95-106.- Fig 1. Geographical location of District 20 Tehran- Table 1- Fuzzy membership of sub-criteria in resilience of District 20 of Tehran against environmental hazards- Fig 2. Diagram of the steps of the work method in the present study- Table 1- The weight of research criteria in resilience of District 20 of Tehran against environmental hazards- Tab 3- Weight of criteria socio-economic in the resilience of Tehran's 20th districtFigure 3- Zoning of population and y

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.