Persian
Language
Geographical Research is Published in Persian Fulltext.
Volume 39, Issue 2 (2024)
GeoRes 2024, 39(2): 117-127 |
Back to browse issues page
Article Type:
Subject:
History
Received: 2024/01/14 | Accepted: 2024/03/14 | Published: 2024/06/5
Received: 2024/01/14 | Accepted: 2024/03/14 | Published: 2024/06/5
How to cite this article
Mobasheri M, Miri G, Sharifinia Z. Strategies for Mitigating Vulnerability of Critical Urban Arteries to Flooding within Bojnurd City. GeoRes 2024; 39 (2) :117-127
URL: http://georesearch.ir/article-1-1573-en.html
URL: http://georesearch.ir/article-1-1573-en.html
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rights and permissions
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
1- Department of Geography and Urban Planning, Zahedan Branch, Islamic Azad University, Zahedan, Iran
2- Department of Geography and Tourism Planning, Sari Branch, Islamic Azad University, Sari, Iran
2- Department of Geography and Tourism Planning, Sari Branch, Islamic Azad University, Sari, Iran
Abstract (792 Views)
Aims: The fundamental pillars and structures crucial to any community are the vital arteries or infrastructures, encompassing all essential facilities and utilities. If these are disrupted or damaged, it will have a significant impact on the health, safety, security, and economy of the society. The objective of this study was to evaluate the flood risk and analyze the critical arteries of Bojnurd city.
Methodology: A research of applied nature was carried out in 2022 within Bojnurd city. The Random Forest algorithm was employed to propose strategies for reducing the susceptibility of city arteries to floods. Through the research, 100 locations prone to flooding and 100 maps free from flooding were identified. Fourteen factors influencing flooding were considered, such as elevation, slope, direction, precipitation, geology, river density, population density, residential density, distance from floodplains, land use, vegetation cover index, topographic land slope index, and moisture index. The significance of each factor was determined by calculating the information gain ratio.
Funding: The variables of elevation, precipitation, and land utilization exerted a notable influence on the occurrence of flooding within the urban area of Bojnurd. Moreover, an analysis encompassing an area of 676 hectares pinpointed the regions characterized by the most elevated susceptibility to floods, while 852 hectares were recognized as exhibiting the least vulnerability. It was observed that a considerable portion of the residential zones face a heightened likelihood of being affected by flood events.
Conclusion: The interconnected nature of street networks, meticulous consideration of street surfacing materials, strategic positioning of buildings, smaller block dimensions, and the implementation of a grid layout featuring regular intersections play a crucial role in mitigating the effects of flooding.
Methodology: A research of applied nature was carried out in 2022 within Bojnurd city. The Random Forest algorithm was employed to propose strategies for reducing the susceptibility of city arteries to floods. Through the research, 100 locations prone to flooding and 100 maps free from flooding were identified. Fourteen factors influencing flooding were considered, such as elevation, slope, direction, precipitation, geology, river density, population density, residential density, distance from floodplains, land use, vegetation cover index, topographic land slope index, and moisture index. The significance of each factor was determined by calculating the information gain ratio.
Funding: The variables of elevation, precipitation, and land utilization exerted a notable influence on the occurrence of flooding within the urban area of Bojnurd. Moreover, an analysis encompassing an area of 676 hectares pinpointed the regions characterized by the most elevated susceptibility to floods, while 852 hectares were recognized as exhibiting the least vulnerability. It was observed that a considerable portion of the residential zones face a heightened likelihood of being affected by flood events.
Conclusion: The interconnected nature of street networks, meticulous consideration of street surfacing materials, strategic positioning of buildings, smaller block dimensions, and the implementation of a grid layout featuring regular intersections play a crucial role in mitigating the effects of flooding.
Keywords:
References
1. Abedini M, Khoshkhovi Delshad A (2016). Investigating the factors affecting the occurrence of floods in the Hawaiq basin using the ANP model. Proceedings of the 1st International Conference on Iranian Natural Hazards and Environmental Crises, Strategies and Challenges. Ardabil: University of Mohaghegh Ardabili. [Persian] [Link]
2. Ahmed N, Hoque MAA, Arabameri A, Pal SC, Chakrabortty R, Jui J (2022). Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network. Geocarto International. 37(25):8770-8791. [Link] [DOI:10.1080/10106049.2021.2005698]
3. Blaikie P, Cannon T, Davis I, Wisner B (2010). At risk: Natural hazards, people's vulnerability and disasters. 2nd edition. London: Routledge. [Link]
4. Bostani A, Javani Kh (2013). Investigation and recognition of natural hazards (earthquake and flood) and its effects on the vulnerability of villages (case study of villages in Darab city). Proceedings of the second International Conference on Environmental Hazards. Tehran: Kharazmi University. [Persian] [Link]
5. Chen W, Zhai G, Fan C, Jin W, Xie Y (2017). A planning framework based on system theory and GIS for urban emergency shelter system: A case of Guangzhou, China. Human and Ecological Risk Assessment: An International Journal. 23(3):441-456. [Link] [DOI:10.1080/10807039.2016.1185692]
6. Cheraghi Ghalehsari A, Habibnejad Roshan M, Roshun SH (2020). Flood susceptibility mapping using a Support Vector Machine models (SVM) and Geographic Information System (GIS). Journal of Natural Environment Hazards. 9(25):61-80. [Persian] [Link]
7. Fleischmann AS, Fialho Brêda JP, Rudorff C, Dias de Paiva RC, Collischonn W, Papa F, et al (2021). River flood modeling and remote sensing across scales: Lessons from Brazil. In: Schumann GJP, editor. Earth observation for flood applications: Progress and perspectives. Amsterdam: Elsevier. p. 61-103. [Link] [DOI:10.1016/B978-0-12-819412-6.00004-3]
8. Ghobadi F, Khodashenas SR, Mosaedi A (2019). Comparison of uniform rainfall method and alternative block method in estimating runoff collecting system in order to control flood in urban areas with ASSA software (case study: Chehel Bazeh Golestan Basin). Iranian Journal of Irrigation and Drainage. 13(5):1491-1503. [Persian] [Link]
9. Hosseini FS, Choubin B, Mosavi A, Nabipour N, Shamshirband S, Darabi H, et al (2020). Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. Science of the Total Environment. 711:135161. [Link] [DOI:10.1016/j.scitotenv.2019.135161]
10. Lee S, Kim JC, Jung HS, Lee MJ, Lee S (2017). Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk. 8(2):1185-1203. [Link] [DOI:10.1080/19475705.2017.1308971]
11. Leta BM, Adugna D, Wondim AA (2024). Comprehensive investigation of flood-resilient neighborhoods: The case of Adama City, Ethiopia. Applied Water Science. 14(2):14. [Link] [DOI:10.1007/s13201-023-02053-7]
12. Mabrouk M, Han H, Abdrabo KI, Mahran MGN, AbouKorin SAA, Nasrallah S, et al (2024). Spatial congruency or discrepancy? Exploring the spatiotemporal dynamics of built-up expansion patterns and flood risk. Science of the Total Environment. 915:170019. [Link] [DOI:10.1016/j.scitotenv.2024.170019]
13. Omidvarfar S, Hosseini L, Fakhari Z, Ghanbari S (2021). Zoning and flood risk assessment in Tabriz city using Fuzzy AHP model. Proceedings of the 2nd International Conference and 5th National Conference on Natural Resources and Environment. Ardabil: University of Mohaghegh Ardabili. [Persian] [Link]
14. Pahlavan Rad MR, Toomanian N, Khormali F, Brungard CW, Komaki CB, Bogaert P (2014). Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma. 232-234:97-106. [Link] [DOI:10.1016/j.geoderma.2014.04.036]
15. Poortaheri M (2010). Application of multi attribute decision making methods in geography. 1st edition. Tehran: SAMT Publications. [Persian] [Link]
16. Poortaheri M, Hajinejad A, Fatahi A, Nemati R (2014) Physical vulnerability assessment of rural habitats against natural hazards (earthquakes) with a decision model (KOPRS) (case study Chalan Cholan villages, Dorud Township). The Journal of Spatial Planning. 18(3):29-52. [Persian] [Link]
17. Saffari A, Ahmadabadi A, Sedighifar Z (2020). Flood risk analysis based on the WMS model in urban catchments (case study: Damand basins, Golabdareh and Saadabad, Tehran metropolitan area). Journal of Applied Research in Geographical Sciences. 20(57):317-334. [Persian] [Link] [DOI:10.29252/jgs.20.57.317]
18. Salvo G, Karakikes I, Papaioannou G, Polydoropoulou A, Sanfilippo L, Brignone A (2024). Enhancing urban resilience: Managing flood-induced disruptions in road networks. [Link] [DOI:10.21203/rs.3.rs-3908598/v1]
19. Sanaifard A, Amirahmadi A, Zanganeh Y (2023). Reducing the vulnerability of vital arteries to flood (case study: Sabzevar County). Research and Urban Planning. 14(52):1-16. [Persian] [Link]
20. Sowmya K, John CM, Shrivasthava NK (2015). Urban flood vulnerability zoning of Cochin City, southwest coast of India, using remote sensing and GIS. Natural Hazards. 75(2):1271-1286. [Link] [DOI:10.1007/s11069-014-1372-4]
21. Statistical Yearbook of Bojnurd Governorate (2022). Bojnurd: North Khorasan Standard General Administration. [Persian] [Link]
22. Taherkhani M, Sojasi Qeydari H, Sadeghloo T (2012). Comparative assessment of ranking methods for natural disasters in rural regions (case study: Zanjan Province). Journal of Rural Research. 2(7):31-54. [Persian] [Link]
23. Tehrany MS, Jones S, Shabani F (2019). Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. CATENA. 175:174-192. [Link] [DOI:10.1016/j.catena.2018.12.011]
24. UNDRR (2015). The human cost of weather-related disasters 1995-2015. Geneva: United Nations Office for Disaster Risk Reduction. [Link]
25. Wang Z, Li Z, Wang Y, Zheng X, Deng X (2024). Building green infrastructure for mitigating urban flood risk in Beijing, China. Urban Forestry & Urban Greening. 93:128218. [Link] [DOI:10.1016/j.ufug.2024.128218]