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ارزیابی تأثیر عامل‌های محیطی در استعداد سیل گیری آبخیز سیروان براساس رخدادهای تاریخی سیل | ||
| پژوهش های آبخیزداری | ||
| مقاله 7، دوره 37، شماره 1 - شماره پیاپی 142، فروردین 1403، صفحه 80-97 اصل مقاله (2.88 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2023.361544.1527 | ||
| نویسندگان | ||
| امید رحمتی* 1؛ آیدینگ کرنژادی2؛ بهرام چوبین3؛ ابوالفضل جعفری4؛ عطا امینی5 | ||
| 1استادیار پژوهشی، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان کردستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، سنندج، ایران | ||
| 2استادیار پژوهشی، بخش تحقیقات منابع طبیعی، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، گرگان، ایران | ||
| 3استادیار پژوهشی، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان اصفهان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اصفهان، ایران | ||
| 4استادیار پژوهشی، مؤسسه ی تحقیقات جنگل ها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| 5استاد پژوهشی، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان کردستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، سنندج، ایران | ||
| چکیده | ||
| مقدمه و هدف هرساله سیل خسارت‌های مالی و تلفات جانی زیادی دارد که مدیریت آن از رکن‌های ضروری مدیریت آبخیزها به‌شمار می‌آید. در این پژوهش استعداد سیل گیری آبخیز سیروان در استان کردستان بررسی شد و در نهایت براساس رخدادهای تاریخی سیل، اهمیت عامل‌های مختلف محیطی در وضعیت استعداد سیل گیری تعیین شد. مواد و روش‌ها از مدل بیشینه‌ی بی نظمی همراه با 13 عامل زمینه ساز پستی‌بلندی، آب‌شناختی، آب‌ریخت‌سنجی (مورفوهیدرولوژیک)، زمین شناختی و محیطی مؤثر بر رخداد سیلاب، استفاده شد. واحد محاسبه‌ای سلولی (پیکسل) به‌عنوان معیار تهیه‌ی نقشه‌ی عامل‌های محیطی و نقشه‌ی استعداد سیل‌گیری انتخاب شد. به عنوان متغیر هدف در مدل، 123 رخداد تاریخی و قابل ملاحظه‌ی سیل گیری در بازه‌ی زمانی 1402-1390 شناسایی و استفاده شد. برای بررسی نتایج مدل رخدادها به دو دسته یادگیری (70%) و اعتبارسنجی (30%) طبقه‌بندی شد. از معیار مساحت زیر منحنی مشخصه‌ی عملکرد گیرنده (AUC) نیز برای ارزیابی عملکرد مدل استفاده شد. نتایج نتایج ارزیابی دقت مدل نشان داد که مساحت زیر منحنی مشخصه‌ی عملکرد گیرنده در دو مرحله‌ی یادگیری و اعتبارسنجی به ترتیب 98/2 و 97/3% به‌دست آمد که بیان‌گر عملکرد عالی مدل بود. بر اساس تفسیر چشمی نقشه‌ی استعداد سیل گیری، مشخص شد که آبراهه های با رتبه‌ی بیشتر در نزدیکی خروجی مجرای عبور جریان با حجم بیشتر، در مناطق پست تر بودند، در نتیجه استعداد سیل گیری بیشتری داشتند. بر اساس نتایج آزمون اهمیت نسبی عامل‌ها، چهار عامل فاصله از آبراهه، شاخص رطوبت پستی‌بلندی، تراکم زهکشی و کاربری زمین‌ها به ترتیب با مشارکت 17، 13، 12 و 10% به‌عنوان مهمترین عامل‌های مؤثر در فرآیند مدل‌سازی استعداد سیل گیری معرفی شدند. این یافته نشان داد که عامل‌های طبیعی (آب‌شناختی و ریخت‌شناختی آب) و محیطی (شامل طبیعی و انسان ساخت) در افزایش استعداد سیل گیری باهم تأثیر دارند. براساس تحلیل‌های کمی به‌دست آمده از مدل‌سازی، 0/76% (5600 هکتار) از منطقه‌ی مطالعه‌شده در طبقه استعداد زیاد و خیلی زیاد سیل گیری بودند که این عرصه نیازمند برنامه‌ریزی و مدیریت سیل است. نتیجه گیری و پیشنهادها جداسازی دقیق و طبقه بندی استعداد سیل در سطح آبخیز سیروان استان کردستان و تعیین اندازه‌ی اهمیت عامل‌های محیطی در رخداد سیل گیری، این امکان را برای مدیران فراهم می‌کند تا با برنامه‌ریزی امکانات و زیرساخت‌های امدادی، گام مؤثری در رویکرد پیشگیرانه بردارند. مدیریت بحران سیل آبخیز سیروان باید مبتنی بر چهار عامل اصلی شناخته‌شده در این پژوهش برنامه‌ریزی شود تا ریسک ناشی از سیل گیری کاهش یابد. برای مدیریت سیل آبخیزها، استفاده از مدل بیشینه بی‌نظمی در استعدادیابی رخداد سیل پیشنهاد می‌شود. | ||
| کلیدواژه‌ها | ||
| بیشینه‌ی بی نظمی؛ سیروان؛ مدیریت ریسک؛ مدیریت بحران؛ منحنی مشخصه‌ی عملکرد گیرنده | ||
| عنوان مقاله [English] | ||
| Evaluating the Impact of Environmental Factors on Flood Susceptibility in the Sirwan Watershed Based on Historical Flood Events | ||
| نویسندگان [English] | ||
| Omid Rahmati1؛ Aiding Kornejady2؛ Bahram Choubin3؛ Abolfazl Jaafari4؛ Ata Amini5 | ||
| 1Assistant Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran | ||
| 2Assistant Professor, Natural Resources Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran | ||
| 3Assistant Professor, Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran | ||
| 4Assistant Professor, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization AREEO, Tehran, Iran | ||
| 5Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran | ||
| چکیده [English] | ||
| Introduction and Goal Floods cause loss of life and financial losses every year, and their management is one of the essential elements of watershed management. In this research, an attempt is made to determine the flood susceptibility of the Sirwan watershed and finally the importance of various environmental factors in flood susceptibility based on historical flood events. Materials and Methods In this research, the maximum entropy model along with 13 topographical, hydrological, morpho-hydrological, geological, and environmental flood-affecting factors were used to model the flood susceptibility of the Sirwan watershed and determine the importance and percentage of participation of various factors in the state of flooding potential. A cellular computing unit (pixel) was chosen as the criterion for preparing the predictive factors and flood susceptibility maps. A total of 123 historical flood inundation events detected in the last decade were used as target variables in the model, of which 70% were considered for learning and the remaining 30% for validating the model results. To evaluate the performance of the model, the criterion of the area under the receiver operating characteristic curve was also used. Results The results indicate that the accuracy of learning and validation were 98.2% and 97.3%, respectively, indicating the excellent performance of the model. Based on the visual interpretation of the flood susceptibility map, streams with a higher order near the watershed outlet, which are the conduits for the passage of the flow with a larger volume and are located in lower areas, often have a higher proneness to flood inundation. Based on the results of the relative importance test, the four factors of distance from the stream, topographic wetness index, drainage density, and land use were introduced as the most important factors in the modeling flood susceptibility, with of 17, 13, 12 and 10% participation, respectively. These results show that natural hydrological, morpho-hydrological and environmental factors (both natural and man-made) have a mutual effect in increasing flooding susceptibility. Based on the quantitative analysis of modeling, about 0.76% (5600 hectares) of the studied area is in the high and very high flood susceptibility class, which requires planning and flood management. Conclusion and Suggestions The high classification of flood susceptibility classes in the Sirwan watershed of Kurdistan province and the determination of the importance of environmental factors in the event of flooding make it possible for managers to take an effective preventive approach by planning relief facilities and infrastructure. To reduce the risk of flooding, flood crisis management in the Sirwan watershed should be defined based on the four main factors identified in this study. Application of the maximum entropy model in flood susceptibility analysis is suggested for flood management of watersheds. | ||
| کلیدواژه‌ها [English] | ||
| maximum entropy, Sirwan, risk management, crisis management, receiver operating characteristic curve | ||
| مراجع | ||
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Abdi P. 2007. Investigating the flood potential of Zanjanrud basin with SCS method and geographic information system. Iranian Irrigation and Drainage. 2(17):22–33. (In Persian). Afifi M. 2020. Evaluating factors affecting flood hazards and preparing a map of sensitivity and probability of its occurrence using Shannon's entropy model (Case study: Firozabad River Watershed). Environmental Hazards Management. 6(2):149–167. (In Persian). Arabameri A, Pourghasemi H, Shirani K. 2018. Zoning of flood susceptibility using the new combined method of Bayesian theory of hierarchical analysis process (Case study: Neka Watershed, Mazandaran Province). Ecohydrology. 4(2):447–462. (In Persian). Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Mishra VN, Bhardwaj A, 2021. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Science of the Total Environment. 750(1):141565. https://doi.org/10.1016/j.scitotenv.2020.141565. Avand M, Moradi H, Ramezanzadeh M. 2021. Preparation of flood sensitivity map using generalized Bayesian random forest and linear forest machine learning models. Journal of Environment and Water Engineering. 6(1):83–95. (In Persian). Azadi F, Sedogh S, Ghahroodi M, Shahabi H. 2021. Flood risk sensitivity zoning in Kashkan River Watershed using two models WOE and EBF. Journal of Geography and Environmental Hazards. 9(1):45–60. (In Persian). Azadtalab M, Shahabi H, Shirzadi A, Chapi K. 2021. Flood risk zoning in Sanandaj city using combined models of statistical index and definitive evidence function. Motaleate Shahri. 9(36):27–40. (In Persian). Breiner FT, Guisan A, Bergamini A, Nobis MP. 2015. Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution. 6(10):1210–1218. https://doi.org/10.1111/2041-210X.12403. Bubeck P, Botzen WJW, Aerts JC. 2012. A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Analysis: An International Journal. 32(9):1481–1495. https://doi.org/10.1111/j.1539-6924.2011.01783.x. Cabrera JS, Lee HS. 2020. Flood risk assessment for Davao Oriental in the Philippines using geographic information system based multi criteria analysis and the maximum entropy model. Journal of Flood Risk Management. 13(2):12607. https://doi.org/10.1111/jfr3.12607. Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A. 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment. 651(1):2087-2096. https://doi.org/10.1016/j.scitotenv.2018.10.064. Daliranfirooz H, Mokhtarikhozani F, Soltani S, Mosavi A. 2016. Assessment of damages caused by floods in the watersheds of Qamsar and Gharoud. Journal of Water and Soil Science. 19(74):63–76. (In Persian). Darabi H, Shahedi K, Mardian M. 2017. Preparation of flood risk probability and sensitivity maps using frequency ratio method in Shazand Doab Bridge watershed. Journal of Watershed Engineering and Management. 8(1):68–79. (In Persian). Davoudi Moghaddam D, Haghizadeh A. 2021. Detection of Susceptible Areas to Flooding and its Most Important Contributing Factors Using the Maximum Entropy Model in the Tashan Watershed, Khuzestan. Watershed Management Research. 33(4):94-109. (In Persian). Ebrahimi L. 2020. Preparing a flood risk management map using the new random forest algorithm (Case study: Lavasanat Watershed). Environmental Hazards Management. 7(2):181–196. (In Persian). Elith J, Graham C, Anderson R, Dudík M, Ferrier S, Guisan A, Zimmermann N. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Echography. 29(2):129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 17(1):43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x. Entezari M, Jalilian T, Darvishikhatoni J. 2019. Zoning of the flood susceptibility map using the evaluation of the efficiency of the methods of frequency ratio and weight of evidence (Case study: Kermanshah Province). Journal of Spatial Analysis Environmental Hazards. 6(4):143–162. (In Persian). Eslaminezhad SA, Eftekhari M, Mahmoodizadeh S, Akbari M, Hajieliasi A. 2022. Evaluation of tree-based artificial intelligence models in order to predict flood risk in GIS platform. Iran-Water Resources Research. 17(2):189–174. (In Persian). Esmaili A, Abdolahi K. 2013. Watershed management and soil conservation. Mohaghegh Ardebili Press. 578 p. (In Persian). Frattini P, Crosta G, Carrara A. 2010. Techniques for evaluating the performance of landslide susceptibility models. Engineering geology. 111(1-4):62-72. https://doi.org/10.1016/j.enggeo.2009.12.004. Ghasemlounia R, Utlu M. 2021. Flood prioritization of basins based on geomorphometric properties using principal component analysis, morphometric analysis and Redvan’s priority methods: A case study of Harşit River Basin. Journal of Hydrology. 603 p.127061. https://doi.org/10.1016/j.jhydrol.2021.127061. Gholami A, Vafakhah M, Alavi S. 2020. Evaluation of different statistical models in preparation of Golestan province flood map. Journal of Range and Watershed Management. 72(4):1011–1022. (In Persian). Golshan M, Esmaili Ori A, Khosravi K. 2019. Evaluation of the flood sensitivity of the Talar watershed using the probabilistic frequency ratio model. Journal of Natural Environmental Hazards. 7(15):1–16. (In Persian). Harrell Jr, Lee KL, Mark DB. 1996. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine. 15(4):361–387. Hosmer Jr, DW, Lemeshow S. 2013. Applied logistic regression (Vol. 398). John Wiley and Sons. Khosravi K, Marofinia A, Nohani A, Chapi K. 2017. Evaluating the effectiveness of the logistic regression model in the preparation of the flood sensitivity map. Journal of Range and Watershed Management. 69(4):863–876. (In Persian). Koehorst BAN, Kjekstad O, Patel D, Lubkowski Z, Knoeff JG, Akkerman GJ. 2005. Work package 6 determination of socio-economic impact of natural disasters. Assessing Socioeconomic Impact in Europe. 173 p. Kornejady A, Ownegh M, Bahremand A. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena. 152(1):144–162. https://doi.org/10.1016/j.catena.2017.01.010. Lechowska E. 2018. What determines flood risk perception? A review of factors of flood risk perception and relations between its basic elements. Natural Hazards. 94(3):1341-1366. https://doi.org/10.1007/s11069-018-3480-z. Mahmood S, Rahman AU. 2019. Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan. Environmental Earth Sciences. 78(43):1-16. https://doi.org/10.1007/s12665-018-8041-y. Mojededi Reizei H, Habibnejad Roshan M, Shahedi K, Pradhan B. 2021. The effectiveness of the combined model of the frequency ratio of the support vector machine in identifying flood-prone areas of the Kalat watershed. Ecohydrology. 7(1):77–95. (In Persian). Nohani A, Darabi F, Marofinia E, Khosravi K. 2017. Evaluation of Shannon's entropy model in preparation of flood sensitivity and probability map in Haraz watershed. Journal of Natural Environmental Hazards. 5(10):99–116. (In Persian). Norani H, Shahedi K, Habibnejad Roshan M, Kavian A, Faramarzi M. 2020. Studying flood sensitivity in Razavar Watershed using Analytical Hierarchy Method (AHP). Journal of Natural Environmental Hazards. 8(19):35–50. (In Persian). Oh HJ, Kim YS, Choi JK, Park E, Lee S. 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology. 399(3-4):158-172. https://doi.org/10.1016/j.jhydrol.2010.12.027. Opolot E. 2013. Application of remote sensing and geographical information systems in flood management: a review. Research Journal of Applied Sciences Engineering and Technology. 6(10):1884–1894. Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190(3):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026. Phillips SJ, Dudík M, Schapire RE. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning. 83 p. Phillips SJ, Dudík M. 2008. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography. 31(2):161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x. Pontius JR, RG, Schneider LC. 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment. 85(1-3):239–248. https://doi.org/10.1016/S0167-8809(01)00187-6. Pourali SH, Arrowsmith C, Chrisman N, Matkan AA, Mitchell D. 2016. Topography wetness index application in flood-risk-based land use planning. Applied Spatial Analysis and Policy. 9(1): 39–54. https://doi.org/10.1007/s12061-014-9130-2. Rahmati O, Pourghasemi HR. 2017. Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models. Water Resources Management. 31(1):1473-1487. https://doi.org/10.1007/s11269-017-1589-6. Razavi Terme S, Pourghasemi H, Dadganfard F. 2019. Preparation of flood potential map using decision-making methods of hierarchical analysis and event weight statistical model (Case study: Jahrom City, Fars Province). Journal of Watershed Management Research. 9(17): 67–81. (In Persian). Rogers K, Woodroffe CD. 2016. Geomorphology as an indicator of the biophysical vulnerability of estuaries to coastal and flood hazards in a changing climate. Journal of Coastal Conservation. 20(1):127-144. https://doi.org/10.1007/s11852-016-0424-1. Satarzadeh E, Sarraf A, Hajikandi H, Sadeghian MS. 2022. Flood hazard mapping in western Iran: Assessment of deep learning vis-à-vis machine learning models. Natural Hazards. 111(1):1–19. https://doi.org/10.1007/s11069-021-05098-6. Shahabi H. 2022. Flood sensitivity zoning in the northern regions of Iran using advanced data mining algorithms (Study area: Haraz Watershed). Journal of Regional Planning. 11(41):165–182. (In Persian). Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O. 2018. Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto International. 33(9):927–941. https://doi.org/10.1080/10106049.2017.1316780. Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S. 2018. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment. 615(1):438–451. https://doi.org/10.1016/j.scitotenv.2017.09.262. Tierney KJ, Lindell MK, Perry RW. 2002. Facing the unexpected: Disaster preparedness and response in the United States. Disaster Prevention and Management: An International Journal. 11(3):222–222. https://doi.org/10.1108/dpm.2002.11.3.222.1. Vafakhah M, Mohammad Hasani Loor S, Pourghasemi H, Katebikord A. 2020. Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arabian Journal of Geosciences. 13(417): 1–16. https://doi.org/10.1007/s12517-020-05363-1. Youssef AM, Pradhan B, Sefry SA. 2016. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences. 75(12):1–12. https://doi.org/10.1007/s12665-015-4830-8. Zhao G, Pang B, Xu Z, Yue J, Tu T. 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment. 615(1):1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037. | ||
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آمار تعداد مشاهده مقاله: 296 تعداد دریافت فایل اصل مقاله: 126 |
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