Determination of Flood Prone Areas with FR, SI and Shannon Models in Order to Reduce Flood Risks (Case Study: Kashkan Watershed)

Document Type : Research Article


1 Associate Professor, Faculty of New Sciences and Technologies, University of Tehran, Iran

2 Assistant Professor, Department of Water Engineering, Lorestan University, Iran

3 PhD Student in water structures, Faculty of Agriculture and Natural Resources, Lorestan University, Iran

4 PhD Student in Watershed Science and Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan


The mapping of flood-prone areas for the purpose of storing run-off to supply the water needed for various purposes, as well as controlling flood damage, shows the importance and necessity of this issue in order to protect natural and human resources. The Lorestan province and especially the Kashkan basin, including: Selseleh, Delfan, Doreh, Khorramabad, Poldakhtar and Kuhdasht, are very flooded and have suffered flood damages many times and in April 2019 had the biggest flood of the last 200 years. In this research, an attempt has been made to map flood zonation in order to reduce flood hazards in Kashkan watershed using frequency ratio models, statistical index and Shannon entropy and also using ArcGIS based methods to improve the decision. Provide flood control and management in this area. For this purpose, the geographical location of 123 floodplains in the region were divided into two groups: calibration and validation. In the implementation of all three models, effective parameters in floods including: slope, slope direction, land curvature, geology, land use, soil science, topographic moisture index, precipitation, waterway density, distance from waterway and digital elevation model of the region were used. The ROC curve in SPSS software was also used to validate the model results. The highest accuracy for this region was assigned to Shannon entropy model (0.82, very good) and then the frequency ratio model and statistical index (0.78, good) were introduced as suitable for this region. The results show that Shannon entropy model shows a larger area of ​​the basin under conditions of high flood risk potential (about 40% of the area in the flood risk category is high and very high) that most of the western areas as well as the central areas of the basin which are located in Kuhdasht, Khorramabad and Poldakhtar. Due to the fact that these areas were introduced to the Kashkan basin in recent studies with other methods, they were introduced as more prone.


[1]. Alizadeh A. Principles of Applied Hydrology, Imam Reza University Press. Principles of Applied Hydrology, Imam Reza University Press, 2009; 26th edition (In Persian).
[2]. Barkhordari J, Tireh Shabankareh K, Mehrjerdi MZ, Khalkhali M. Study of water spreading effects on quantitative and qualitative changes of pastural cover: A case study in station of Sarchahan water spreading (Hormozgan province). Watershed Researches in Pajouhesh & Sazandegi. 2009; 82: 65-72 (In Persian).
[3]. Azadi F, Sadough H, Qahrdavi M, Shahabi H. Zoning of flood risk sensitivity in Kashkan river basin using two models EBE and WOE. Journal of Geography and Environmental Hazards, 2020, 33: 45-60. [Persian].
[4]. Ismaili Alavicheh A, Karimi S, Alavipour F. Vulnerable assessment of urban areas against floods with fuzzy logic, Quarterly Journal of Environmental Science and Technology, 2017. 5: 12-1. [Persian].
[5]. Arianpour, M. and Jamali, A. A. Flood Hazard Zonation using Spatial Multi-Criteria Evaluation (SMCE) in GIS (Case Study: Omidieh-Khuzestan). European Online Journal of Natural and Social Sciences. 2015, 4(1): 39 – 49.
[6]. Khosravi, K., Pourghasemi, H. R., Chapi, K., & Bahri, M. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental monitoring and assessment. 2016, 188(12), 656.
[7]. Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., Bui, D. T., Pham, B. T., & Khosravi, K. A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 2017, 95, 229-245.
[8]. Abedini M, Fathi M. Flood Risk Mapping and Evaluation by using the Analytic Network Process Case Study: Khiav Chai Catchment. Hydrogeomorphology. 2015; Volume 1, Issue 3, Page 81-97. [Persian].
[9]. Hosseini M, Jafar Biglou M, Ground F. Determination of flood catchment areas of Kashkan river using hydraulic model to reduce flood risks. Journal of Risk Knowledge, 2015. 2 (3): 355-369. [Persian].
[10].            Siahkamari S, Zinivand H. Potential finding of flood prone areas using statistical index model and weight of evidence (Maderso watershed, Golestan). Journal of Remote Sensing and GIS in Natural Resources. 2016, 7 (4): 116-133. [Persian].
[11].            Abedini M, Beheshti Javid E. Flood Hazard Mapping of Lighvan Chai Watershed Using Network Analysis Process Model (ANP) and GIS. Geographic Space, Islamic Azad University of Ahar Branch. 2016; 55: 293-312. [Persian].
[12].            Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M. A New Approach to Flood Susceptibility Assessment in Data-Scarce and Ungauged Regions Based on GIS-based Hybrid Multi CriteriaDecision-Making Method, Journal of Hydrology 2019; Volume 572, pp 17-31.
[13].            Bui, D. T., Panahi, M., Shahabi, H., Singh, V. P., Shirzadi, A., Chapi, K., Ahmad, B. B. (2018). Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific reports, 8(1), 15364.
[14].            Azad Talab M, Shahabi H, Chapi K, Shirzadi A. Prediction of flood risk in Sanandaj city using hybrid models. Master Thesis in Environmental Risk, 2019, University of Kurdistan. [Persian].
[15].            Tehrany MS, Jones S, Shabani F. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena. 2019; 1(175):174-92.
[16].            Faramarzi H, Hosseini M, Pourghasemi H, Farnaghi M. Flood risk assessment and zoning in Golestan National Park. Journal of Echo Hydrology, 2019. 6 (4): 1055-1068. [Persian].
[17].            Mokhtari D, Rezaei Moghadam, Rahimpour T, Moezz S. Preparation of flood risk map in Gomnab Chay catchment using model ANP and techniques GIS. Journal of Echo Hydrology. 2020, 7 (2): 502-497. [Persian].
[18].            Hosseinzadeh M, Panahi R, Tarband T. Flood sensitivity zoning in Songhor catchment in Kermanshah province. Journal of Echo Hydrology. 2020, 7 (4): 873-889. [Persian].
[19].            Moore ID, Grayson RB, Ladson AR. Digital terrain modelling. A review of hydrological, geomorphological, and biological applications. 1991, Hydrol Process 5, 3–30.
[20].            Bui D, Lofman O, Revhaug I, Dick O. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards. 2011, 59(3), 1413–44.
[21].            Bednarik M, Magulova B, Matys M, Marschalko M. Landslide Susceptibility Assessment of the Kral ovany–Liptovsky Mikulas Railway Case Study. J. Physics and Chemistry of the Earth. 2010, 35(3-5): 162-171.
[22].            Entezari M, Jalilian T, Darvish Khatouni J. 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. 2019, 6 (4): 143-160.
[23].            Tahmasebipour N, Rahmati O, Ghorbaninejad S. Predicting the sensitivity of geyser erosion in Seymareh region based on the Hamel model of certainty and determining the importance of factors affecting it, Echo Hydrology. 2016, 3 (1): 83-93. [Persian].
[24].            Tehrany M. S, Pradhan B, Jebur M. N. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of hydrology. 2014, 512, 332-343.
[25].            Tehrany MS, Pradhan B, Jebur MN. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment. 2015, 29(4): 1149-1165.
Volume 8, Issue 1
April 2021
Pages 307-319
  • Receive Date: 05 November 2020
  • Revise Date: 31 January 2021
  • Accept Date: 31 January 2021
  • First Publish Date: 14 March 2021
  • Publish Date: 21 March 2021