ارزیابی خطر سیل و عوامل مؤثر بر آن در حوضه آبخیز زهر-جراحی در جنوب غرب ایران با استفاده از روش سلسله‌مراتبی فازی

نوع مقاله : مقاله کامل

نویسندگان

گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران

10.22059/jphgr.2024.376692.1007829

چکیده

با توجه به وقوع سیلاب‌های خسارت زا در اکثر حوضه‌های آبخیز کشور، ضرورت ایجاد سامانه‌های پیش‌بینی و پهنه‌بندی در این حوضه‌ها بیش از پیش موردتوجه قرارگرفته است. در این پژوهش، خطر سیل‌خیزی در حوضه آبریز زهره ـ جراحی با استفاده از روش سلسله‌مراتبی فازی (FAHP) و در بازه زمانی ۱۳۸۶-۱۳۹۸ موردبررسی قرارگرفته است. در پهنه‌بندی سیلاب در این حوضه از شاخص‌های کاربری اراضی، شاخص اختلاف نرمال شده پوشش گیاهی (NDVI)، زمین‌شناسی، بارندگی، منحنی رواناب، شاخص قدرت جریان، تراکم زهکشی، فاصله از رودخانه، نوع شکل زمین، انحنای طولی شیب، ارتفاع و شیب استفاده‌شده است. پس از مراحل مختلف اجرای روش فازی هر یک از لایه‌ها با توجه به نوع روابط آن‌ها با پدیده سیل‌خیزی و بر اساس توابع تعیین‌شده مقدار عضویت آن‌ها مشخص گردید. پهنه‌بندی پتانسیل خطر سیلاب در حوضه موردمطالعه بر اساس وزن‌ها و ارزش‌های نهایی هر یک از متغیرها انجام گرفت. نتایج این پژوهش، نشان داد، که گامای ۹/۰ نسبت به سایر عملگرهای همپوشانی فازی (گامای 5/، 7/0، AND و OR) در منطقه شباهت بیشتری را با واقعیت دارد. نتایج این تحقیق نشان داد، که متغیرهای شاخص قدرت جریان، NDVI، بارندگی و زمین‌شناسی بیشترین تأثیر را بر پتانسیل سیل‌خیزی در زیر حوضه‌های منطقه موردمطالعه دارند. با توجه به نتایج به‌دست‌آمده، حدود ۸۶/۶ درصد از مساحت منطقه در کلاس بسیار پرخطر قرار دارند که، عمدتاً در ارتفاعات بلند کوهستانی شرق و جنوب حوضه قرار دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis of Flood Risk and Influencing Factors in Zohr-Jarhari Basin in Zohr-Jarhari in Southwest of Iran using Fuzzy Analytic Hierarchy Process (FAHP) Approach

نویسندگان [English]

  • Reza Zakerinejad
  • Kamal Ayash
Department of Physical Geography, Faculty of Geography Sciences and Planning, University of Isfahan, Isfahan, Iran
چکیده [English]

ABSTRACT
Iran has experienced many extreme flood events in the last century. The necessity of creating prediction and zoning systems in most of the watersheds of this country has been given more attention than ever before. The purpose of the study focused on flood risk mapping and the risk index assessment based on the GIS-FAHP-multi-criteria decision-making process. Thirteen risk-relevant variables, including both quantitative (such as precipitation, elevation, slope, plan curvature, NDVI, distance from the river, drainage density, SPI, TWI, and CN) and qualitative factors (such as land use, landform, and geology). The raster maps of the investigated variables were prepared using ESRI ArcGIS software. The fuzzy overlay function (AND, OR AND GAMMA) has been applied after each layer for fuzzification with proper fuzzy function in ArcGIS10.8. The result of different fuzzy operations indicates that gamma 0.9 showed more accuracy when compared to the flood events than other fuzzy overlapping operators in the region. The resulting map showed that mountain areas and areas with high slopes in the north and northwest of the study area were very susceptible to flooding and were classified in the high flash flood susceptibility class.
Extended Abstract
Introduction
Floods are the most frequent type of natural disaster and occur when an overflow of water submerges land that is usually dry. Floods have the most significant damage potential of all-natural disasters worldwide and affect the greatest number of people. Flood risk management has proven successful at reducing the threat of some flooding in order to reduce and control these damages. A flood can occur when water enters the watershed too quickly for the land to absorb; on the other hand, a flood is the maximum water flow of a watershed. Extreme flooding events are not relegated to the least developed nations but can devastate and ravage the most economically advanced and industrialized nations. Floods harmfully affected crops and their production in many areas of the world. Flooding and heavy rain have caused problems for people across parts of Iran. Our study area is located southwest of Iran and has been affected by several flood events in recent decades. This area has a complex topography, dry climate, and poor vegetation that causes it to be more susceptible to flood risk. One Keelung city of Taiwan research study applied a geographic information system (GIS) and artificial neural network (GANN) model for flood susceptibility assessment. Various factors were used, including elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index. The results show that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area, including the city's financial center, can be categorized as high to very high flood susceptibility zones. This article aims to analyze flood risk and influencing factors in the Zohr-Jarhari Basin in the Southwest of Iran using the Fuzzy analytic hierarchy process (FAHP) approach.
 
Methodology
The Zohr-Jarhari Basin (Southwest of Iran) is very susceptible to floods. This catchment drains into the Persian Gulf and was selected as a study area because of its high population density and resulting exposition to hazards. This region is situated in the southwest of Iran, ranging from 48°16′ to 52°16′ N and 29°46′ to 31°40′ E, and covers an approximate area of 41.014 km². The area is located at the interface between the over-thrust and the folded Zagros, following the over-thrust Zagros structurally. The altitude of the area varies from 0 to 3639 m a.s.l., with the Zohreh-Jarahi catchment comprising 24 sub-catchments. Although most of the sub-catchments are located in Khuze stan province in southwest Iran, some parts of the study area are also found in Fars, Kohgiluyeh, and Boyer-Ahmad provinces. Therefore, the present study focused on flood risk mapping and the risk index assessment based on the GIS-FAHP-multi-criteria decision-making process. Thirteen risk-relevant variables, including both quantitative (such as precipitation, elevation, slope, plan curvature, NDVI, distance from the river, drainage density, SPI, TWI, and CN) and qualitative factors (such as land use, landform, and geology). The raster maps of the investigated variables were prepared using ESRI ArcGIS software. The fuzzy overlay function (AND, OR AND GAMMA) has been applied after each layer for fuzzification with proper fuzzy function in ArcGIS10.8.
 
Results and Discussion
This study created Flash flood susceptibility maps using the effective factors in flood susceptibility and the FAHP (Fuzzification and Criteria Weights (%)) algorithms. After weighting the criteria and sub-criteria examined in the research in order to zone the flood potential in the watershed, the sub-criteria layers of the research using fuzzy operators Gamma 0.5, Gamma 0.7, Gamma 0.5, AND, Sum Algebra (OR) have been used. The result of different fuzzy operations indicates that gamma 0.9 showed more accuracy when compared to the flood events than other fuzzy overlapping operators in the region. The resulting map showed that mountain areas and areas with high slopes in the north and northwest of the study area were very susceptible to flooding and were classified in the high flash flood susceptibility class. The analysis of the final weights of the FAHP shows that the Stream Power Index variables with correlation coefficient, NDVI, rainfall and geology have the greatest impact on the flood potential in the study area.
 
Conclusion
Meanwhile, the use of modern technologies and new methods of water resources management and a better understanding of the climatic condition of the region can improve the situation for the society and the environment. This study combined Fuzzy and AHP algorithms to create a flash flood susceptibility map using flash flood conditioning factors. The study shows the important role of GIS in the decision-making process. The maps in the current study indicated that mountain heights in the east and southeast of the basin are vulnerable to flooding and must be a preference for management to stop alleviating flash flooding. These flash flood susceptibility maps could be used to reduce the future harm made by flash floods, to help the disaster management processes in the future, to improve greater methods for preservation, to extend the research, and to develop flash flood predictions and precaution systems. Meanwhile, the use of modern technologies and new methods of water resources management and a better understanding of the climatic condition of the region can improve the situation for the society and the environment.
 
Funding
 There is no funding support.
 
Authors’ Contribution
 All of the authors approved thecontent of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.
 

کلیدواژه‌ها [English]

  • Flooding
  • Flood Risk
  • Fuzzy Analytic Hierarchy Process
  • Zohreh-Jahrahi Watershed
  1. احمدی، حسن. (1378). ژئومورفولوژی کاربردی، جلد اول، فرسایش آبی. تهران: انتشارات دانشگاه تهران.
  2. اسماعیلی، رضا و طاهری، محمد. (۱۴۰۱). ارزیابی مناطق مستعد خطر سیلاب با نگرش فازی، مطالعه موردی: پایین‌دست حوضه آبریز نکا-استان مازندران. مجله مخاطرات محیط طبیعی. 11(34)، ۱۵۸-145.  DOI:10.22111/JNEH.2022.39817.1842
  3. آوند، محمدتقی؛ سعید، جانی زاده و جعفری، فائزه. (۱۳۹۹). ارزیابی روش‌های یادگیری ماشین در تهیه نقشه احتمال خطر سیل. تخریب و احیاء اراضی طبیعی، سال اول، شماره ۱، ۳۲-۱۹. 20.1001.1.27174425.1399.1.1.4.3.  DOI:
  4. بدری، بهرام؛ زارع بیدکی، رفعت؛ هنربخش، افشین و آتشخوار، فاطمه. (1395). اولویت‌بندی زیرحوضه‌های آبخیز بهشت‌آباد از نظر پتانسیل سیل‌خیزی. پژوهش‌های جغرافیای طبیعی. 48 (1)، 158-143. DOI: 10.22059/jphgr.2016.57032
  5. حسنوند، شکوفه؛ سپه وند، علیرضا؛ ترنیان، فرج اله و سیهاک، پروین. (1400). ارزیابی مدل های نفوذپذیری در خاک سطحی  سازندهای زمین‌شناسی در آبخیز الشتر، استان لرستان. پژوهش‌های آبخیزداری، 34(4)، 164-150. DOI:10.22092/WMRJ.2021.354035.1398
  6. حجازی، سید اسدالله و لقمان نیا، کوثر. (1402). پهنه‌بندی زمانی و مکانی خطر سیل‌خیزی در حوضه آبریز کَرگانرود با استفاده از مدل AWBM و روش.Fuzzy-ANP '  پژوهش‌های جغرافیای طبیعی. 55 (3)، 71-88. DOI: 10.22059/jphgr.2023.361608.1007778
  7. ذاکری نژاد، رضا. (1399). ارزیابی روش‌های رقومی ارتفاع جهت تهیه نقشه پتانسیل فرسایش خندقی با استفاده از روش مکسنت و سامانه اطلاعات جغرافیایی (مطالعه موردی: حوضه آبخیز سمیرم، جنوب استان اصفهان). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی،11 (3)،106-122. DOI:10.30495/GIRS.2020.674955
  8. زیاری، کرامت الله؛ رجایی، سید عباس و داراب خانی، رسول (۱۳۹۹). پهنه‌بندی پتانسیل سیل‌خیزی با استفاده از تحلیل سلسله‌مراتبی و منطق فازی در محیط GIS نمونه موردی: شهر ایلام. دو فصلنامه مدیریت بحران. ۱۹، ۳۰-۲۱. DOI: 20.1001.1.20085656.1401.15.58.2.0
  9. طاهری بهبهانی، محمد طاهر و بزرگ‌زاده، مصطفی. (۱۳۷۵). سیلاب‌های شهری. انتشارات مرکز مطالعات و تحقیقات شهرسازی و معماری ایران.
  10. علیزاده، امین. (۱۳۸۷). اصول هیدرولوژی کاربردی. انتشارات دانشگاه امام رضا (ع).
  11. قنواتی، عزت اله. (1392). پهنه‌بندی خطر سیلاب شهر کرج با استفاده از منطق فازی. جغرافیا و مخاطرات محیطی، 2(8)، 113-132.
  12. یمانی، مجتبی. (۱۳۸۴). ارتباط ویژگی‌های ژئومرفولوژیک حوضه‌ها و قابلیت سیل خیری (تجزیه‌وتحلیل داده‌های سیل از طریق مقایسه ژئومرفولوژیک حوضه فشنه و بهجت‌آباد). مجله پژوهش‌های جغرافیایی. 38 (3)، 57-47.
  13. Ahmadi, H. (2011. Applied Geomorphology. Water Erosion. 3rd Edition, Tehran University Press, Tehran. [In Persian].
  14. Alizadeh, A. (2013). The Principles of Applied Hydrology. 36th Edition, Imam Reza (AS) University, Mashhad. [In Persian].
  15. Avand, M., Janizadeh, S., & Jafari, F. (2020). Evaluating the Efficiency of Machine Learning Models in Preparing Flood Probability Mapping. Degrad Rehabil Nat Land, 1 (1), 19-32. DOI: 20.1001.1.27174425.1399.1.1.4.3. [In Persian] 
  16. Aher, P. D., Adinarayana, J., & Gorantiwar, S. D. (2014). Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: a remote sensing and GIS approach. Journal of Hydrology, 511, 850-860. ‌doi:10.1016/j.jhydrol.2014.02.028.
  17. Abtahee, M., Islam, A. A., Haque, M. N., Zonaed, H., Ritu, S. M., Islam, S. M. I., & Zaman, A. (2023). Mapping Ecotourism Potential in Bangladesh: The Integration of an Analytical Hierarchy Algorithm and Geospatial Data. Sustainability, 15(15), 11522. doi:10.3390/su151511522
  18. Acharya, A., Mondal, B. K., Bhadra, T., Abdelrahman, K., Mishra, P. K., Tiwari, A., & Das, R. (2022). Geospatial analysis of geo-ecotourism site suitability using AHP and GIS for sustainable and resilient tourism planning in West Bengal, India. Sustainability, 14(4), 2422. doi:10.3390/su14042422.
  19. Badri, B., Zare Bidaki, R., Honarbakhsh, A., & Atashkhar, F. (2016). Prioritization of Flooding Potential in Beheshtabad Subbasins. Physical Geography Research. 48(1): 143-158. doi: 10.22059/jphgr.2016.57032. [In Persian].
  20. Costache, R. (2019). Flood susceptibility assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management. Water Resources Management, 33(9), 3239-3256. doi: org/10.1007/s11269-019-02301-z.
  21. Esmaili, R., & Taheri, M. (2022). Evaluation of flood hazards areas with fuzzy approach, Case study: Downstream of Neka catchment, Mazandaran province. Journal of Natural Environmental Hazards. 11, 34(4):145-158. [In Persian]. doi:10.22111/JNEH.2022.39817.1842.
  22. Ghanavati, E. (2020). Flood Risk Zonation for Karaj City Using Fuzzy Logic. Journal of Geography and Environmental Hazards. 2(4):113-132. [In Persian].
  23. Ha, J., & Kang, J. (2022). Assessment of flood-risk areas using random forest techniques: Busan Metropolitan City. Nat Hazards.
  24. Lohani, A. K., Goel, N. K., & Bhatia, K. K. S. (2014). Improving real time flood forecasting using fuzzy inference system. Journal of hydrology, 509, 25-41. doi: org/10.1007/s11069-021-05142-5.
  25. Hasanvand, S., Sepahvand, A., Tarnian, F., & Sihag, P. (2022). An Assessment of Infiltration Models in the Surface Soil of Geological Formations in Aleshtar Watershed, the Province of Lorestan. Watershed Management Research Journal. 34, 4 133: 150-164. (In Persian) doi:10.22092/WMRJ.2021.354035.1398.
  26. Hejazi, S. A., & Loghmannia, K. (2023). Temporal and spatial zoning of flood risk in Karganrood catchment using AWBM model and Fuzzy-ANP method. Physical Geography Research, 55(3):71-88. doi: 10.22059/jphgr.2023.361608.1007778. [In Persian]
  27. Lane, S. N. (2017). Natural flood management. Wiley Interdisciplinary Reviews: Water, 4(3), e1211. ‌
  28. Jaafar, H. H., Ahmad, F. A., & El Beyrouthy, N. (2019). GCN250, new global gridded curve numbers for hydrologic modeling and design. Scientific data, 6(1), 1-9.
  29. Kalantar, B., Ueda, N., Saeidi, V., Janizadeh, S., Shabani, F., Ahmadi, K., & Shabani, F. (2021). Deep neural network utilizing remote sensing datasets for flood hazard susceptibility mapping in Brisbane, Australia. Remote Sensing, 13(13), 2638.‌ doi.org/10.3390/rs13132638.
  30. Khoirunisa, N., Ku, C. Y., & Liu, C. Y. (2021). A GIS-based artificial neural network model for flood susceptibility assessment. International Journal of Environmental Research and Public Health, 18(3), 1072. doi: 10.3390/ijerph18031072
  31. Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., ... & Prakash, I. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311-323. ‌ doi:10.1016/j.jhydrol.2019.03.073. 
  32. Mokarram, M., & Hojati, M. (2017). Using ordered weight average (OWA) aggregation for mutli-criteria soil fertililty evalution by GIS (case study: southwest Iran). Computers and electronics in agriculture, 132, 1-13. doi.org/10.1016/j.compag.2016.11.005.
  33. Meles, M. B., Younger, S. E., Jackson, C. R., Du, E., & Drover, D. (2020). Wetness index based on landscape position and topography (WILT): Modifying TWI to reflect landscape position. Journal of environmental management, 255, 109863. ‌doi.org/10.1016/j.jenvman.2019.109863
  34. Nguyen, H. T., & Sugeno, M. (Eds.). (2012). Fuzzy systems: modeling and contro, 2. Springer Science & Business Media.
  35. Parsian, S., Amani, M., Moghimi, A., Ghorbanian, A., & Mahdavi, S. (2021). Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets. Remote sensing,13 (47761), 1-22. doi:10.3390/rs13234761.
  36. Poff, N. L., Bledsoe, B. P., & Cuhaciyan, C. O. (2006). Hydrologic variation with land use across the contiguous United States: geomorphic and ecological consequences for stream ecosystems. Geomorphology, 79(3-4), 264-285. doi.org/10.1016/j.geomorph.2006.06.032.
  37. Shirani, K., & Zakerinejad, R. (2021). Watershed prioritization for the identification of spatial hotspots of flood risk using the combined TOPSIS-GIS based approach: a case study of the Jarahi-Zohre catchment in Southwest Iran. AUC Geographica 56(1), 120–128.  doi:10.14712/23361980.2021.6
  38. Sugianto, S., Deli, A., Miswar, E., Rusdi, M., & Irham, M. (2022). The Effect of Land Use and Land Cover Changes on Flood Occurrence in Teunom Watershed, Aceh Jaya. Land, 11(8), 1271. ‌ doi:10.3390/land11081271.
  39. Tanim, A. H., McRae, C. B., Tavakol-Davani, H., & Goharian, E. (2022). Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning. Water, 14(7), 1140. doi.org/10.3390/w14071140.
  40. Tripathi, P. (2015). Flood disaster in India: an analysis of trend and preparedness. Interdisciplinary Journal of Contemporary Research, 2(4), 91-98.
  41. Taheri Behbahani, M.T., & Bozurzadeh, M. (1996). Urban floods. Publications of Iran's Urban Planning and Architecture Studies and Research Center,
  42. Yaseen, A., Lu, J., & Chen, X. (2022). Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model. Stochastic Environmental Research and Risk Assessment, 36(10), 3041-3061. doi.org/10.1007/s00477-022-02179-1.
  43. Zakerinejad, R. (2020). Evaluation of of DEMs to the modeling of the potential of gully erosion using Maxent model (Case study: Semirom catchment in the south of Isfahan Province, Iran). journal of rs and GIS for natural resources (journal of applied rs and gis techniques in natural resource science), 11(3 (40):106-122. [In Persian]. doi:10.30495/GIRS.2020.674955.
  44. Ziari, K., Rajai, S.A., & Darabkhani, R. (2022). Flood Zoning Using Hierarchical Analysis andFuzzy Logic in GISCase Study: Ilam City. Journal of Emergency Management 10(1) 19. DOI: 20.1001.1.20085656.1401.15.58.2.0. [In Persian].