کاهش آسیب‏پذیری شریان‏های حیاتی در برابر سیل (مطالعه موردی: شهرستان سبزوار)

محورهای موضوعی : فصلنامه علمی و پژوهشی پژوهش و برنامه ریزی شهری

ابوالقاسم سنائی فرد ¹ (دانشجو دکتری ژئومورفولوژی، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران.)
ابوالقاسم امیر احمدی ² (استاد ژئومورفولوژی، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران.)
یعقوب زنگنه ³ (دانشیار گروه برنامه‌ریزی شهری، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران.)

تاریخ دریافت : 1399/08/24 تاریخ پذیرش : 1400/05/22 تاریخ انتشار : 1402/02/01

کلید واژه: آسیب‌پذیری, سیلاب, سبزوار, مدیریت بحران, مدل ماشین بردار پشتیبان,

چکیده مقاله :

سیل یکی از مخرب‌ترین بلایای طبیعی است که معمولا با خسارت‌های جانی و مالی فراوانی همراه است. سیل همه ساله در کشور ایران خسارات زیادی را به منابع اقتصادی وارد می‌آورد. وقوع سیل و خسارات ناشی از آن در ایران طی سال-های اخیر روند افزایشی داشته است. هدف از این پژوهش، بررسی میزان خطر پذیری سیلاب و ارزیابی خسارات وارده به شهر سبزوار می‌باشد. جهت بررسی سیلاب از مدل ماشین بردار پشتیبان برای مدل‌سازی سیلاب استفاده شد. پس از بررسی‌های صورت گرفته 56 نقطه سیل گیر ثبت شده و 10 عامل موثر در وقوع سیل شامل کاربری اراضی، تراکم جمعیت، مسیل‌ها، طبقات شیب، تراکم مسکونی، ضریب CN، ضزیب رواناب، تراکم جمعیت، فضای باز و قدمت ابنیه در نظر گرفته شد. سپس جهت برآورد میزان رواناب در منطقه مورد مطالعه از روش‌ جاستین و I.C.A.R استفاده گریده است و طبق منحنی پوش و دیکن براورد سیلاب منطقه در دوره بازگشت 25 ساله و دوره بازگشت 50 ساله جهت دوره-های بازگشت برآورد گردید و نتایج نشان داد که مقدار بارندگی در سطح منطقه توسط مدل به ‌سرعت قابل ‌تبدیل به رواناب و پس از روندیابی، به بده و تراز آب در مقاطع مورد نظر می‌باشد. ارزیابی مدل توسط معیارهای مختلف سنجش دقت از جمله ضریب کاپا، ریشه میانگین مربعات خطا، منحنی مشخصه عملکرد سیستم ROC و منحنی میزان پیش-بینی صورت گرفت. هدف اصلی پژوهش بررسی سیلاب و عومل موثر در وقوع آن‌ها و در نهایت شناسایی مناطق مستعد سیلاب با استفاده از مدل ماشین بردار پشتیبان جهت کاهش بحران و آسیب‌پذیری سیلاب شهری می‌باشد. با توجه به مطالعات انجام شده مشخص گردید سیل بندهای ضلع شمال شهر با مسیل‌های موجود در شهر سبزوار منطبق نمی‌باشد به طوری که شبکه معابر شهری بیش از شبکه مسیل‏ها سبزوار در هدایت سیلاب شهری نقش دارند، پس نیاز است مسیل‏های مناسب جهت هدایت آبهای جاری و سیلاب‌ها در شهرستان سبزوار ایجاد شود تا نقش مسیل‌ها در هدایت سیل بیشتر از معابر شهری گردد. نتایج به دست آمده از این پژوهش می‌تواند برای مدیریت مناطق آسیب‌پذیر سیل و کاهش بحران استفاده شود.

چکیده انگلیسی:

As the most devastating natural disasters, floods are often associated with significant loss of life and property. Every year, floods in Iran cause a lot of damage to economic resources. The occurrence of floods and the resulting damage in Iran has been increasing in recent years. The study aims to investigate the risk of floods and assess the damage to Sabzevar city. To study the flood, the support vector machine model was used for flood modeling. After investigating 56 recorded flood points, ten factors influencing floods' occurrence, including land use, population density, canals, slope classes, residential density, CN coefficient, runoff rate, population density, open space, and age of buildings were considered. Then, Justin and ICAR methods were employed to estimate the study area runoff. According to the envelope and Deacon curve, the flood in the region was evaluated in 25-year and 50-year return periods. The results showed that the model's amount of rainfall could be quickly converted into runoff and after routing to yield and water level in the desired sections. The model was evaluated by various accuracy measures, including kappa coefficient, RMSE, system ROC curve performance and prediction rate curve (PRC). The study aims to investigate floods and the factors affecting their occurrence and identify areas prone to floods using the support vector machine (SVM) model to reduce the crisis and vulnerability to urban floods. According to the studies, it was found that the flood dams on the north side of the city do not correspond to the existing problems in Sabzevar city so that the network of urban thoroughfares is more involved in directing urban floods than the network of Sabzevar canals. Therefore, it is necessary to create suitable channels for directing running water and floods in Sabzevar city so that the role of channels in leading floods is more than urban roads. The results of this study can be used to manage flood vulnerable areas and reduce crises.

منابع و مأخذ:

1. Abu Reza Towfiqul Islama Swapan Talukdarb Susanta Mahatob Sonali Kundua Kutub Uddin Eibeka Quoc Bao Phamcd Alban Kuriqie Nguyen Thi ThuyLinhfg,(2021): Flood susceptibility modelling using advanced ensemble machine learning models, Geoscience Frontiers, 12(3). https://doi.org/10.1016/j.gsf.2020.09.006


2. Singh, A., Dawson, D., Trigg, M., & Wright, N. (2021): A review of modelling methodologies for flood source area (FSA) identification. Natural Hazards, 1-22.


3. Baghalani, M., Rostami, N., & Tavakoli, M. (2019). Identification of effective factors on the occurrence of urban floods in Ilam watershed, 11(2), 523-536. (In Persian)


4. Behnam Ghasemzadeh, Z. S., Zarabadi, S., Majedi, H., Behzadfar, M., & Ayyoob Sharif (2021): A Framework for Urban Flood Resilience Assessment with Emphasis on Social, Economic and Institutional Dimensions: A Qualitative Study Sustainability, 13, 7852. https://doi.org/10.3390/su13147852 https://www.mdpi.com/journal/sustainability


5. Cao, C., Xu, M., Kamsing, P., Boonprong, S., Yomwan, P., Saokarn, A. (2021). Flooding Identification by Vegetation Index. In Environmental Remote Sensing in Flooding Areas; Springer: Singapore, 29–44


6. Quagliolo, C., Comino, E., & Pezzoli, A. (2021); Experimental Flash Floods Assessment Through Urban Flood Risk Mitigation (UFRM) Model: The Case Study of Ligurian Coastal Cities, Front. Water, 31 May 2021 | https://doi.org/10.3389/frwa.2021.663378 1-16


7. Cheraghi Ghale Sari, A., Habibnejad Roshan, M., & Roshan, S. H. (2020): Preparation of flood sensitivity map using support vector model (SVM) and geographic information system (GIS). Journal of Environmental Hazards, 9(25), 61-78. (In Persian)


8. Sarkar, D., & Mondal, P. (2020); Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region, Appl Water Sci, 10(1), 17


9. Darfashi, K. B., Adeli, S. F., & Malek Mohammadi, B. (2020); Providing a model in the analysis and zoning of the level of vulnerability of urban areas in the risk of floods in Tehran (case study: Districts 1 & 2 of Tehran Municipality), Journal of Crisis Management. 10(17), 5-16. (In Persian)


10. Ding, Y., Liu, Y., and Song, Y. (2020); East Asian summer monsoon moisture transport belt and its impact on heavyrainfalls and floods in China, Advances in Water Resources, 629-643, https://doi.org/10.14042/j.cnki.32.1309.2020.05.001


11. Farhadi, H., & Esmaili, K. (2019); Evaluation of the ability of a machine training method in estimating the maximum flood discharge due to dam failure, Iranian Journal of Irrigation and Water Engineering, 9(36), 1-13. (In Persian)


12. Fath Alizad, B., Abedini, M., & Rajabi, M. (2019); Investigation of the causes of floods and their hazards in Zanuzchay catchment using HEC-HMS hydrological model and fuzzy logic, Quantitative Geomorphological Research, 9(2), 134-155. (In Persian)


13. Ghobadi, F., Khodashenas, S. R., & Masaedi, A. (2019); Comparison of two patterns of uniform rainfall and periodic block in the evaluation of runoff collection system to control floods in densely populated urban areas using ASSA software (Case study: Chehl Bazeh Basin, Golestan area of Mashhad), Iranian Journal of Irrigation and Drainage, 13(5), 1491-1503. (In Persian)


14. Hoch, J. M., & Trigg, M. A. (2019); Advancing global flood hazard simulations by improving comparability, benchmarking, and integration of global flood models, Environmental Research Letters, 14, https://doi.org/10.1088/1748-9326/aaf3d3


15. Hosseini, Y. (2013); Selecting a Method for Calculating the Annual Estimation of a Drain Basin by Examining Various Methods for Estimating the Annual Flow (Jaroo Basin). First National Conference on Drainage in Sustainable Agriculture, Tarbiat Modares University.


16. Liu, J., Xiong, J., Cheng, W., Li, Y., Cao, Y., He, Y., Duan, Y., He, W., Yang, G. (2021); Assessment of Flood Susceptibility Using Support Vector Machine in the Belt and Road Region. Natural hazards and earth system sciences, https://doi.org/10.5194/nhess-2021-80 Preprint, CC BY 4.0 License.


17. Kadaverugu, A., Nageshwar Rao, C., and Viswanadh, G. K. (2021): Quantification of flood mitigation services by urban green spaces using InVEST model: a case study of Hyderabad city, India. Model. Earth Syst. Environ. 7, 589–602. doi: 10.1007/s40808-020-00937-0


18. Li, Q., Jiang, X., & Liu, D. (2013); Analysis and modelling of flood risk assessment using information 755 diffusion and artificial neural network, Water SA, 39, 634-648, https://doi.org/10.4314/wsa.v39i5.8.


19. Moreira, L. L., de Brito, M. M., & Kobiyama, M. (2021); A systematic review and future prospects of flood vulnerability indices. Natural Hazards and Earth System Sciences, 21(5), 1513-1530.


20. Sahana, M., Rehman, S., Sajjad, H., & Hong, H. (2020): Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India. Catena, 189, 104450.


21. Malekzadeh, S., Arman, A., & Azari, A. (2020); Flood hydrograph trend detection using Mike11 numerical model and support vector machine (Case study: Seymareh River), 21(78), 79-98. (In Persian)


22. Masri Alamdari, P. (2021); Spatial analysis of flood risk in Ajabshir tea castle catchment using GIS and HEC-HMS. Quantitative Geomorphological Research, 10(1), 93-111. (In Persian)


23. Mir Emadi, S. Z., Karami, H., Hosseini, Kh., & Hashemi, S. A. A. (2019); Flood reduction in urban watersheds using LID-BMPS in SWMM model and selection of superior option by AHP-TOPSIS method (Study Case: Golestan town of Semnan), 6(4), 1003-1013. (In Persian)


24. Mirasdollahi, Sh. S., & Janbaz Ghobadi, Gh.R. (2020); Analysis of resilience of urban settlements against floods with emphasis on economic and social indicators (Case study: Gorgan City), (59), 137-155. (In Persian)


25. Mishra, A., Arya, D.S. (2020); Development of Decision Support System (DSS) for Urban Flood Management: A Review of Methodologies and Results. In World Environmental and Water Resources Congress 2020: Water, Wastewater, and Stormwater and Water Desalination and Reuse; American Society of Civil Engineers: Reston, VA, USA, 60–72


26. Mohanty, M.P., Nithya, S., Nair, A.S., Indu, J., Ghosh, S., Bhatt, C.M., Rao, G.S., Karmakar, S. (2020); Sensitivity of various topographic data in flood management: Implications on inundation mapping over large data-scarce regions. J. Hydrol., 590, 125523.


27. Mojaddedi Rizeei, H., Habibnejad Roshan, M., Shahedi, K., & Pardahan, B. (2020); Efficiency of Frequency Ratio of Combined Ratio-Vector Machine in Identifying Flood Prone Areas of Kalat Watershed, Journal of Echo Hydrology, 7(1), 77- 95. (In Persian)


28. Kumar, N., Liu, X., Narayanasamydamodaran, S., & Pandey, K. K. (2021); A Systematic Review Comparing Urban Flood Management Practices in India to China’s Sponge City Program. Sustainability, 13(11), 6346.


29. Rashidi, M., & Hosseinzadeh, M. M. (2019); The role of sub-basins overlooking the city in the occurrence of urban floods in Izeh (Khuzestan), 8(29), 25-42. (In Persian)


30. Saffari, A., Ahmadabadi, A., & Sedighifar, Z. (2020); Flood risk analysis based on the WMS model in urban catchments (Case Study: Darband, Golabdereh and Saadabad Basins of Tehran Metropolis), Journal of Applied Research in Geographical Sciences, 20(57), 318-334. (In Persian)


31. Sattari, M. T., Pour-Azad, A., & Mir Abbasi Najafabadi, R. (2016); Technical Report: Predicting Hourly Floods of Ahrachai River Using Machine Learning Methods, Journal of Engineering and Watershed Management, 8(1), 115-127. (In Persian)


32. Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., Shirzadi, A. (2018); Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J. Environ. Manag. 217, 1–11


33. Shahabi, H. (2021); Flood susceptibility zoning in northern regions of Iran using advanced data mining algorithms (Case Study: Haraz Watershed), Quarterly Journal of Regional Planning of Marvdasht, 11(2), 167-184. (In Persian)


34. Siasar, H., & Honar, T. (2019); Application of support vector machine models, chad and random forest in estimating daily reference transpiration evaporation in the north of Sistan and Baluchestan province, Iranian Journal of Irrigation and Drainage, (2), 388-378. (In Persian)


35. Tahmasebi, M. R., Shabanloo, S., & Rajabi, A. (2021); Zoning of flood probability using a comparative study of two known models of random forest and support vector machine in northern Iran, Journal of Water and Irrigation Management, 11, 1 -15. (In Persian)


36. Vahabzadeh, Gh., Miraki, Sh., & Shirzadi, A. (2017); Landslide sensitivity zoning with GIS and comparison of efficiency of logistic regression methods and frequency ratio (Case study: Cheshmidar watershed, Kurdistan), Journal of Information System Application Geography and Remote Sensing in Planning, 8(2), 11-21. (In Persian)


37. Zanganeh Asadi, M. A., Amir Ahmadi, A., & Naemi Tabar, M. (2021); Evaluation of efficiency of Vicor, L-THIA and artificial neural network models in regional flood analysis (Case study: Khorasan Razavi province), Journal of Echo Hydrology, 8(1), 108-89. (In Persian)