Preparation of flood hazard potential map using two methods: Frequency Ratio and Statistical Index (Case study: Aji Chai Basin)

Document Type : Applied Article


1 Professor of Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran

2 Postdoctoral researcher in Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran



Floods are considered one of the most important and abundant geomorphic hazards in the country, which cause a lot of damage every year. Aji Chai basin, located in East Azerbaijan province, witnesses devastating floods every year due to its large area and special topographical conditions. Therefore, the main aim of this study is to prepare a flood hazard potential map in this basin. To achieve this aim, 18 parameters effective in the occurrence of this hazard and two statistical methods of frequency ratio (FR) and statistical index (SI) have been used. The investigated parameters were Elevation, Slope, Aspect, Rainfall, Distance to the river, River density, Normalized Difference Vegetation Index, land use, Distance to bridge, Distance to dam, lithology, hydrological soil groups, Topographic wetness index, Sediment transport index, Stream power index, Drainage texture, Geomorphology and earth curvature. The location of the past flood points in the area was used to determine the parameters weight and implement the research models. The final maps were prepared from the product of the weights of each parameter class in their information layers and were classified into five classes using the Natural Breaks tool. Study the final maps showed that hazard zones spatial distribution patterns were similar in both models. In this way, the areas downstream of the basin and around the main streams of the area are the most dangerous zones. The accuracy evaluation of the results of both models with the ROC curve showed that the values of the area under the curve for SI and FR models were 0.945 and 0.919, respectively, which shows the excellent performance of both models in preparing flood hazard maps.


[1] آزادطلب، مهناز؛ شهابی، هیمن؛ شیرزادی، عطااله؛ و چپی، کامران (1399). پهنه‌بندی خطر سیلاب در شهر سنندج با استفاده از مدل‌های ترکیبی شاخص آماری و تابع شواهد قطعی. مطالعات شهری، 36، 27-40.
[2] آزادی، فهیمه؛ صدوق، سید حسن؛ قهرودی، منیژه؛ و شهابی، هیمن (1399)، پهنه‌بندی حساسیت خطر سیل در حوضۀ آبخیز رودخانه کشکان با استفاده از دو مدل WOE و EBF، جغرافیا و مخاطرات محیطی، 33، 45-60.
[3] پیرستانی، محمدرضا؛ و شفقتی، مهدی (1388). بررسی اثرات زیست‌محیطی احداث سد. جغرافیای انسانی، 1(3)، 39-50.
[4] حبیبی، محمدرضا؛ پاک‌باز، حمید؛ و صفایی کوچکسرایی، علیرضا (1397). بررسی پارامترهای اساسی در ساخت سازۀ آبگذر (پل) در مسیر رودخانه، مهندسی آب، 6(2)، 124-131.
[5] حجاریان، احمد (1402). مطالعه تطبیقی مدل‌سازی مناطق حساس به وقوع سیل (استان اصفهان). مدیریت مخاطرات محیطی، 10‌(3)، 199-214.
[6] رحیم‌پور، توحید؛ رضائی‌مقدم، محمدحسین؛ حجازی، سید اسدالله؛ و ولیزاده کامران، خلیل (1400). تحلیل تغییرات فضایی حساسیت خطر وقوع سیل بر پایه نوعی مدل ترکیبی نوین (مطالعۀ موردی: حوضۀ آبریز الندچای، شهرستان خوی). مدیریت مخاطرات محیطی، 8‌(4)، 371-393.
[7] رضائی‌مقدم، محمدحسین؛ حجازی، سیداسدالله؛ ولیزاده کامران، خلیل؛ و رحیم‌پور، توحید (1399). بررسی حساسیت سیل‌خیزی حوضه‌های آبریز با استفاده از شاخص‌های هیدروژئومورفیک (مطالعۀ موردی: حوضۀ آبریز الندچای، شمال غرب ایران). پژوهش‌های ژئومورفولوژی کمی، 9(2)، 195-214. 10.22034/gmpj.2020.118241
[8] معروفی‌نیا، ادریس؛ نوحانی، ابراهیم؛ خسروی، خه‌بات؛ و چپی، کامران (1395). ارزیابی روش شاخص آماری در تهیۀ نقشۀ حساسیت به وقوع سیل. دانش آب‌وخاک، 26‌(2)، 201-214.
[9]. Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., Parvinnejhad, D., Shahabi, H., & Panahi, M. (2019). Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int, 34 (11), 1252–1272.
[10]. Ajim Ali, Sk., Farhana Parvin, Bao Pham, Q., Vojtek, M., Vojteková, J., Costache, R., Thi Thuy Linh, N., Quan Nguyen, H., Ahmad, A., & Ghorbani, M.A. (2020). GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia. Ecological Indicators, 117.
[11]. Aydin, H.E., Iban, M.C. (2023). Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations. Nat Hazards, 116, 2957–2991.
[12]. Band, S.S., Janizadeh, S., Chandra Pal, S., Saha, A., Chakrabortty, R., Melesse, A.M., & Mosavi, A. (2020). Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sensing, 12 (21).
[13]. Bisht, S., Chaudhry, S., Sharma, S., & Soni, S. (2018). Assessment of flash flood vulnerability zonation through Geospatial technique in high altitude Himalayan watershed, Himachal Pradesh India. Remote Sensing Applications: Society and Environment, 12, 35-47.
 [14]. Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash Flood Hazard
Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine
Subsidence Areas. Sustainability, 8(9), 948.
 [15]. Cloke, H.L., & Pappenberger, F. (2009). Ensemble flood forecasting: a review. Journal of Hydrology, 375(3), 613–626. doi:
[16]. Costache, R. (2019). Flood susceptibility assessment by using bivariate statistics and machine learning models: a useful tool for flood risk management. Water Resour Manage, 33(9), 3239– 256.
[17]. Dankers, R., Arnell, N.W., Clark, D.B., Falloon, P.D., Fekete, B.M., Gosling, S.N., Heinke, J., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., & Wisser, D. (2014). First look at changes in flood hazard in the inter-sectoral impact model intercomparison project ensemble. Proc. Natl. Acad. Sci, 111, 3257–3261.
[18]. Das, S. )2019(. Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sensing Applications: Society and Environment, 14, 60-74. doi:
[19]. Fernandez, D., & Lutz, M. (2010). Urban flood hazard zoning in Tucum_an Province, Argentina, using GIS and multicriteria decision analysis. Eng. Geol, 111(1), 90–98.
[20]. Gittleman, M., Farmer, C.J., Kremer, P., & McPhearson, T. (2017). Estimating stormwater runoff for community gardens in New York City. Urban Ecosyst, 20 (1), 129–139.
[21]. Glenn, E., Morino, K., Nagler, P., Murray, R., Pearlstein, S., & Hultine, K. (2012). Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J. Arid Environ, 79, 56–65.
[22]. Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., &  Kanae, S. (2013). Global flood risk under climate change. Nat. Clim. Chang, 3, 816.
[23]. Kiss, R. (2004). Determination of drainage network in digital elevation model, utilities and limitations. J. Hung.vGeo-Math, 2, 16–29.
[24]. Kourgialas, N.N., & Karatzas, G.P. (2011). Flood management and a GIS modelling method to assess flood- hazard areas—a case study. Hydrological Sciences Journal, 56(2), 212–225. doi:
[25]. Lee, S., & Pradhan, B. (2006). Probabilistic landslide hazards and risk mapping on Penang Island. Malaysia. Journal of Earth System Science, 115(6), 661–672.
[26]. Lee, S., Kim, J. C., J, H. S., Lee, M.J., & Lee, S. (2017). Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Nat. Hazards Risk, 8, 1185–1203.
[27]. Majeed, M., Lu, L., Anwar, M.M., Tariq, A., Qin, S., El-Hefnawy, ME., El-Sharnouby, M., Li, Q., & Alasmari, A. (2023). Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms. Front. Environ. Sci, 10, 1-14. doi: 10.3389/fenvs.2022.1037547.
[28]. Moore, I.D., Grayson, R.B., & Ladson, A.R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process, 5, 3–30.
[29]. Msabi, M.M., & Makonyo, M. (2021). Flood susceptibility mapping using GIS and multi-criteria decision analysis: A case of Dodoma region, central Tanzania. Remote Sensing Applications: Society and Environment, 21,
[30]. Norman, L., Huth, H., Levick, L., Shea Burns, I., Phillip Guertin, D., Lara‐Valencia, F., & Semmens, D. (2010). Flood hazard awareness and hydrologic modelling at Ambos Nogales, United States–Mexico border. Journal of Flood Risk Management, 3(2), 151-165.
[31]. Oztekin, B., & Topal, T. (2005). GIS-based detachment susceptibility analyses of a cut slope in Limestone, Ankara-Turkey. Environ. Geol, 49, 124–132.
[32]. Papaioannou, G., Vasiliades, L., & Loukas, A. (2015). Multi-criteria analysis framework for potential flood prone areas mapping. Water Resour. Manag, 29, 399–418.
[33]. Paul, G.C., Saha, S., & Hembram, T.K. (2019). Application of the GIS-Based Probabilistic Models for Mapping the Flood Susceptibility in Bansloi Sub-basin of Ganga-Bhagirathi River and Their Comparison. Remote Sensing in Earth Systems Sciences, 2, 120–146.
[34]. Powell, S.J., Jakeman, A., & Croke, B. (2014). Can NDVI response indicate the effective flood extent in macrophyte dominated floodplain wetlands? Ecological Indicators, 45, 486–493.
[35]. Rahmati, O., Pourghasemi, H.R., & Zeinivand, H. (2016). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31, 42–70.
[36]. Saha, S., Arabameri, A., Saha, A., Blaschke, T., Ngo, P.T.T., Nhu, V.H., & Band, S.S. (2021). Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method. Science of the total environment, 764,
[37]. Saikh, N.I., & Mondal, P. (2023). GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India. Natural Hazards Research,
[38]. Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., & Rahmati, O. (2017). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto International, 1–15.
[39]. Sofia, G., Roder, G., Dalla Fontana, G., & Tarolli, P. (2017). Flood dynamics in urbanised landscapes: 100 years of climate and humans’ interaction. Scientific Reports, 7, 40527.
[40]. Sujatha, E.R., Selvakumar, R., Rajasimman, U.A.B., & Victor, R. (2015). Morphometric analysis of sub-watershed in parts of Western Ghats, South India using ASTER DEM. Geomatics, Natural Hazards and Risk, 6(4), 326-341.
[41]. Wang, Y., Fang, Z., Hong, H., Costache, R., & Tang, X. (2021). Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. Journal of Environmental Management, 289, doi:
[42]. Wu, Y.L., Li, W.P., Wang, Q.Q., Liu, Q.Q., Yang, D.D., Xing, M.L., Pei, Y.B., & Yan, S.S. (2016). Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arabian Journal of Geosciences, 9, 84.
[43]. Yariyan, P., Avand, M., Abbaspour, R.A., Torabi Haghighi, A., Costache, R., Ghorbanzadeh, O., Janizadeh, S., & Blaschke, T. (2020). Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics, Natural Hazards and Risk, 11(1), 2282–2314.
[44]. Zwenzner, H., & Voigt, S. (2009). Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrology and Earth System Sciences, 13:67–576.