نوع مقاله : پژوهشی

نویسندگان

1 دانشیار گروه جغرافیا، دانشگاه خوارزمی تهران، ایران

2 دانشجوی دکتری ژئومورفولوژی دانشگاه خوارزمی تهران، ایران

3 استادیار گروه جغرافیا دانشگاه خوارزمی تهران، ایران

4 استادیارگروه جغرافیا، دانشگاه خوارزمی تهران، ایران

چکیده

سیلاب یکی از مهمترین خطرات طبیعی است که اغلب با تأثیرات عظیم سالانه میلیون‌ها نفر را در سراسر جهان تحت تأثیر قرار می‌دهد. در سال‌های اخیر به دلیل وقوع سیلاب‌هـای مکـرر در حوضۀ آبخیز رودخانه‌ چشمه‌کیله و متعاقـب آن ایجـاد خسـارات ناشـی از سیلاب، لزوم توجه به پهنه‌بندی خطر سیل‌خیزی حوضـۀ مـورد بررسی بیش از پیش نمایان می‌شود. در بین روش‌های مختلف برای تهیه نقشه‌های پهنه‌بندی سیلاب، روش‌های آماری به علت سادگی در عمل و نیز دقت قابل قبول، بیشتر مورد توجه قرار می‌گیرند. هدف از این پژوهش مقایسه قابلیت اعتماد مدل‌های آنتروپی شانون، نسبت فراوانی و وزن شاهد در زمینه پهنه‌بندی سیل‌خیزی حوضه آبخیز چشمه‌کیله است. در این تحقیق از معیارهای شیب، طبقات ارتفاعی، جنس خاک، شاخص رطوبت توپوگرافی، فاصله از رودخانه، زمین‌شناسی، کاربری اراضی، تراکم آبراهه، NDVI و بارندگی استفاده شده است. احتمال رخداد سیلاب برای هر کلاس از هر پارامتر محاسبه شده است. وزن‌های محاسبه شده برای هر کلاس در نرم‌افزار GIS ARCدر لایه‌های مربوطه اعمال شده و نقشه‌های پهنه‌بندی سیلاب منطقه به دست آمد. نقشه‌های نهایی حاصل از اجرای این سه مدل در منطقه به 3 طبقه کم خطر، متوسط و پرخطر تقسیم شدند. و نهایتا قابلیت اعتماد هر یک از مدل‌ها با استفاده از منحنی مشخصه عملکرد سیستم (ROC) ارزیابی شدند. نتایج نشان داده است که تکنیک نسبت فراوانی (FR)، وزن شاهد (WOE) و آنتروپی شانون (SE) به ترتیب اولویت، دارای بیشترین دقت در پیش‌بینی وقوع سیلاب هستند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Adaptive flood zoning in Cheshmekile watershed, Tenkabon

نویسندگان [English]

  • Amir Saffari 1
  • Sara Mohammadi 2
  • Ali Ahmadabadi 3
  • Sahar Darabi 4

1 Associate Professor, Department of Geography, Khorazmi University, Tehran, Iran

2 Ph.D student of Geomorphology, Kharazmi University, Tehran, Iran

3 -Assistant Professor of Department of Geography, Khwarazmi University, Tehran, Iran

4 Assistant Professor of Department of Geography, Khuarazmi University, Tehran, Iran

چکیده [English]

Floods are one of the most important natural hazards that often affect millions of people around the world annually with huge impacts. In recent years, due to the occurrence of frequent floods in the watershed of Cheshmekile River and the subsequent damage caused by floods, the need to pay attention to the zoning of the flood risk in the investigated basin is becoming more apparent. Among the different methods for preparing flood zoning maps, statistical methods are more important due to their simplicity and acceptable accuracy. The aim of this research is to compare the reliability of Shannon entropy models, frequency ratio and witness weight in the context of flood zoning in Cheshmekile watershed. In this research, the criteria of slope, elevation classes, soil type, topographic humidity index, distance from the river, geology, land use, watercourse density, NDVI and rainfall have been used. The probability of flood occurrence has been calculated for each class of each parameter. The calculated weights for each class were applied in the ARC GIS software in the relevant layers and flood zoning maps of the area were obtained. The final maps resulting from the implementation of these three models in the region were divided into 3 low risks, medium and high-risk classes. And finally, the reliability of each model was evaluated using the system performance characteristic curve (ROC). The results have shown that frequency ratio (FR), weight of evidence (WOE) and Shannon entropy (SE) techniques have the highest accuracy in predicting the occurrence of floods.

کلیدواژه‌ها [English]

  • flood
  • frequency ratio (FR)
  • weight of witness (WOE)
  • Shannon entropy (SE)
  • Cheshmekile
  • North of Iran
Entezari, M., Jalilian, T., & Darvish Khatouni, J. (2019). Zoning of flood susceptibility map using performance evaluation of frequency ratio and weight of evidence methods (Kermanshah province). Journal of Spatial Analysis of Environmental Hazards, 6 (4): 143-160. (In Persian)
Kia, A., Khaledi, S., & Janbazghobadi, Gh. (2021). Determination of flood potential effective factors in hydrological homogenous regions, (Case study: Se-Hezar and Do-Hezar watersheds Cheshmehkileh Tonekabon), Volume 10, Issue 38 - Serial Number 38, Pages 235-258. (In Persian)
Mohammadi, M., A., Mohammadi, F., Fakherifard, A., & Bijanvand, S. (2020). Derivation of Rule Curve for Flood Risk Zone A Case Study: Baranduz-Chay River, Volume 7, Issue 22 - Serial Number 22, Pages 87-108, (In Persian)
Madadi, A., Piroozi, E., & Aghayary, L. (2019). Flood Hazard Zonation by Combining SCS-CN and WLC Methods (Case study: Khiyave Chay Meshkinshahr Basin), Volume 5, Issue 17 - Serial Number 17 Pages 85-102. (In Persian)
Nasiri Khiavi, A., Vafakhah, M., & Sadeghi, S., H. (2021). The Impressibility of Flood Regime from Rainfall and Land Use Changes in Cheshmeh Kileh Watershed, Volume 8, Issue 1, Pages 221-234. (In Persian)
Pourghasemi, H., R., Moradi, H., R., & Mohammadi, M. (2014). Landslide Susceptibility Zoning, Using Weight   of Evidence Probabilistic Model. jgit.; 1 (2) :69-80
(In Persian)
Pornaby Darzi, S., Vafakhah, M., & Rajabi, M., R. (2021). Flood hazard zoning using HEC-RAS Hydraulic   Model and ArcGIS (Case Study: CheshmehKileh River in Tonekabon County) Volume 10, Issue 28 - Serial Number 2 September 2021 Pages 15-28. (In Persian)
Rajaei, M. (2018). Hydrogeomorphological modeling of urban flood (Case study: Tankabon Cheshmekile river) Ph.D thesis, Department of Geomorphology, Khuarazmi University. (In Persian)
Rahmani, Sh., Azizian, A., Samadi, A. (2019). Determining the Flood Hazard Level of Mazandaran Sub-Basins Using a GIS-based Distributed Method, Volume 14, Issue 1 - Serial Number 141 Pages 123-139.(In Persian)
Tahmasebipoor, N., Rahmati, O., & Ghorbani Nejad, S. (2016). Prediction of gully erosion susceptibilityin Seimare region using certainty factor model and importance analysis of conditioning factors, Volume 3, Issue 1, Pages 83-93. 10.22059/IJE.2016.59192. (In Persian)
Zayyari, K., Ebrahimipoor, M., Pourjafar, M., R., & salehi, E. (2020). Explaining Strategies for Increasing Physical Resilience against Flood (Case Study: Cheshmeh Kile River, Tonekabon River), Volume 3, Issue 1, Pages 89-105. (In Persian)
Althuwaynee, O., F., Pradhan, B., Park, H., J., & Lee, J., H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114, 21–36.
Chowdhuri, I., Pal Subodh, Ch., & Chakrabortty, R. (2019). Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India, Advances in Space Research.
Dai, FC., Lee, CF., Li, J., & Xu, ZW. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391.
Demir, V., & Kisi, O. (2016). Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey, Advances in Meteorology.
Fernandez, DS., & Lutz, M., A. (2010). Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98
Guo E., L., Zhang Z., Q. & Ren X., H. (2014). Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China. Nat. Hazards Journal, 74: 947–965.
Jie Yang, D., Townsend Yilmaz, C., Topal, T., & Suzen M.L. (2012). GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Science, 65: 2161- 2178.
Jhih, H., W., Gwo-Fong L., Yun-Ru H., I-Hang H., & Chieh-Lin C. (2022). Application of hybrid machine learning model for flood hazard zoning assessments, Stochastic Environmental Research and Risk Assessment.
Kia, MB., Pirasteh, S., Pradhan, B., Mahmud, AR., Sulaiman, WNA., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251– 264.
Lee, M.J., Kang, J.E., & Jeon, S. (2012). Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: Geoscience and Remote Sensing Symposium (IGARSS), Munich. 895–898.
Miller, JR., Ritter DF., & Kochel RC. (1990). Morphometric assessment of lithologic controls on drainage basin evolution in the Crawford Upland, south-central Indiana. Am J Sci 290:569–599. 19.
Nampak, H., Pradhan, B., & Manap, M.,A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, pp.283-300.
Nguyen, B., D, Nguyen, Q., L., Ropesh, G., Dang, T., A, Dang, T., M.(2021).The Role of  Factors Afecting Flood Hazard Zoning Using Analytical Hierarchy Process: A Review Earth Systems and Environment King Abdulaziz University and Springer Nature Switzerland AG.
Pourghasemi, H.,R., Mohammadi, M., & Pradhan, B. (2012). Landslide susceptibility mapping using index of entropy and conditional probability models at Safarood Basin, Iran. Catena 97, 71–84.
Patra, J.,P., Kumar, R., & Mani, P. (2015). Combined fluvial and pluvial flood inundation modeling for a project site, Procedia Technology, 24: 93-100.
Reinhardt- Imjela, Ch., Imjela, R., Beolscher, J., & Schulte, A. (2018). The impact of late medieval deforestation and 20th century  forest  decline  on  extreme  flood  magnitudes  in  the Ore  Mountains  (Southeastern  Germany, Quaternary International 475.
Ramesh, V., & Sumaira S, I. (2020). Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India, Geocarto International, DOI: 10.1080/10106049.2020.1730448
Stefanidis S., & Stathis D.(2013). Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat. Hazards Journal, 68: 569–585.
Stephane H., Colin G., & Robert J., N. (2013). Future flood losses in major coastal cities. Nat. Climate Change Journal, 3(9): 802–806.
Song, K.,Y, J., Oh, J., Choi, I., Park, C., Lee & S., Lee. (2012). Prediction of landslides using ASTER imagery and data mining models. Advances in Space Research, 49: 978-993.
The International Disaster Database (EM-DAT). (2016). http:\www.emdat.beabout
Tehrany, M.S., Pradhan, B., Mansour, Sh., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types.125:91-101.
Yang, J., Ronald, D, T., & Danshfar, B. (2006). Applying the HEC-RAS model and GIS techniques in river network floodplain delineation, can. J. Civ. Eng. NO:33, pp: 19-28.