Flood Risk Spatial Analysis via Integrating SCS-CN and Logistic Regression Methods (Case Study: Kalateh-ye Qanbar Drainage Basin in Nishabur)

Abedini, M., & Lotfi, Kh. (2019). Estimating the height of runoff to analyze flood potential using the curve number method in the Shahrood catchment area of Ardabil, Quarterly of Geographic Space (19) 68. 163-181.

Abu El-Magd, S. A., Maged, A., & Farhat, H. I. (2022). Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact. Environmental Science and Pollution Research, 29 (38), 57345–57356. https://doi.org/10.1007/s11356-022-19903-2.

Aditian, A., Kubotab, T., & Shinoharab, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101–111.

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). GeocartoInternational, 34 (11),1252-1272.

Al-Areeq, A. M., Abba, S. I., Yassin, M. A., Benaafi, M., Ghaleb, M., & Aljundi, I. H. (2022). Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia.  Remote Sensing, 14 (21), 5515. https://doi.org/10.3390/rs14215515.

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.

Bates, P. D., Horritt, M. S., & Fewtrell, T. J. (2010). A simple inertial formulation
of the shallow water equations for efficient two dimensional flood inundation modelling. J. Hydrol, 387, 33–45. doi: 10.1016/j.jhydrol.2010.03.027.

Bui, D. T., Tsangaratos, P., Ngo, P. T. T., Pham, T. D., & Pham, B. T. (2019). Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci. Total Environ, 668, 1038–1054.

Cai, X., & Xu, D. (2022) Simulation and Optimization Strategy of Storm Flood Safety Pattern Based on SCS-CN Model. Int. J. Environ. Res. Public Health, 19, 698. https://doi.org/10.3390/ijerph19020698.

Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J., Zhu, A., Pei, X., & Duan, Z. (2018). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci. Total Environ, 626, 1121–1135. https://doi.org/ 10.1016/j.scitotenv.2018.01.124.

Costache, R., & Bui D. T. (2020). Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Sci. Total Environ, 136492.

Crawford, N. H. (1966). Digital Simulation in Hydrology: Stanford Watershed Model IV; Stanford University Technical Reports; Department of Civil Engineering, Stanford University: Stanford, CA, USA: 39.

Darabi, H., Torabi Haghighi, A., Rahmati, O., Jalali Shahrood, A., Rouzbeh, S., Pradhan, B., & Tien Bui, D. (2021). A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation. Journal of Hydrology, 603, 126854. 1-11.

Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M., Shamshirband, S., & Mosavi, A. (2020) .Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci. Total Environ, 705, 135983.

Dorn, J., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., De Smedt, P., Chu, W., Antrop, M., De Maeyer, P., Finke, P., Van Meirvenne, M., Verniers, J., & Cromb´e, P. (2014) Application of the topographic position index to heterogeneous landscapes. Geomorphology, 186, 39.

Dottori, F., Salamon, P., Bianchi, A., Alfieri, L., Hirpa, F. A., & Feyen, L. (2016). Development and evaluation of a framework for global flood hazard mapping. Adv. Water Resour. 94, 87–102. doi: 10.1016/j.advwatres.2016.05.002.

El-Rawy, M., Elsadek, W. M., & De Smedt, F. (2022). Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression. Water, 14, 2434. https://doi.org/10.3390/ w14152434.

Fang, Sh., Ji, Y., & Zhang, M. (2022). Numerical Modeling the Flood and Pollutant Transport Processes in Residential Areas with Different Land Use Types. Hindawi Advances in Meteorology, 16, Doi.org/10.1155/2022/9320089.

Fanni, Z, & Gheshmi, S. (2017). Spatial zoning and analysis of the four environmental hazards of landslides, floods, earthquakes and subsidence (Case study: 22 districts of Tehran). Journal of Geographical Data (SEPEHR), 27 (4), 76-89.

Garrote, J. (2022). Global DEMs and Flood Modelling - A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique). Water, 14 ­(176), Doi.org/10.3390/w14020176.

Gholami, H., Mohammadifar, A., Golzari, Sh., Torkamandi, R., Moayedi, E., Zare Reshkooeiyeh, M., Song, Y., & Zeeden, Ch (2025). Mapping flood risk using a workflow including deep learning and MCDM– Application to southern Iran. Urban Climate, 59, 102272, https://doi.org/10.1016/j.uclim.2024.102272

Green, C, Diepernk, G., Hegger, D., Pettersson, M., Priest, S., & Tapsell, S. (2014) Flood risk management in Europe: the flood problem and interventions. Star flood, 1- 250.

Guisan, A., Weiss, S. B., & Weiss, A. D. (1999). GLM versus CCA spatial modeling of plant species distribution. Plant Ecol, https://doi.org/10.1023/A:1009841519580

Hawkins, R. H. (1980). "Sit selection and curve numders", Proceeding of symposium on watershed management, ASAE, Littleton, co.USA.

He, X., Chen, Ch., & Liu, Y. (2019). Mountain flooding analysis based on SCS-CN model -- A case study in Mingxi, China, IOP Conf. Series, Materials Science and Engineering, 780, 072048. doi:10.1088/1757-899X/780/7/072048.

Hitouri, S., Mohajane, M., Lahsaini, M., Ali, S. A., Setargie, T. A., Tripathi, G., D’Antonio, P., Singh, S.K., & Varasano, A. (2024). Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco. Remote Sens, 16, 858. https://doi.org/10.3390/rs16050858.

Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Chen, W. (2018). Application of fuzzy weight of evidence and data mining techniques in construction of16 D. TADESSE ET AL.flood susceptibility map of Poyang County, China.Science of the Total Environment, 625, 575–588. https://doi.org/10.1016/j.scitotenv.2017.12.256.

Hong, J. (2025). Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning. Natural Hazards.https://doi.org/10.1007/s11069-025-07109-2.

Hosein, Y. (2022). Evaluation of WMS model in basins without statistical data in southwestern Iran using Dicken's experimental method (case study: Kuhgel Basin of Khuzestan Province). Applied Water Science, 12, 162 https://doi.org/10.1007/s13201-022-01685-5.

Islam, T., Zeleke, E. B., Afroz, M., & Melesse, A. M. A. (2025). Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches. Remote Sens, 17, 524.https://doi.org/10.3390/rs17030524.

Jehanzaib, M., Ajmal, M., Achite, M., & Kim, T. W. (2022). Comprehensive Review: Advancements in Rainfall-Runoff Modelling for Flood Mitigation. Climate, 10, 147. https:// doi.org/10.3390/cli10100147

Kader, Z., Islam, M. R., Aziz, M. T., Islam, M. R., & Miah, M. (2024). GIS and AHP-based flood susceptibility mapping: a case study of Bangladesh.  Sustainable Water Resources Management, 10, 170. https://doi.org/10.1007/s40899-024-01150-y.

Khosravi, K., Melesse, A. M., Shahabi, H., Shirzadi, A., Chapi, K., & Hong, H. (2019). Flood susceptibility mapping at ningdu catchment, China using bivariate and data mining techniques”. Extreme Hydrology and Climate Variability, 419–434, Amsterdam e Netherlands.

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 ofHydrology, 573, 311-323.

Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R.,Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS:Johor River Basin, Malaysia. Environmental Earth Sciences, 67, 251–264. https://doi.org/10.1007/s12665-011-1504-z.

Lee, S. (2004). Application of likelihood ratio andlogistic regression models to landslide susceptibilitymapping using GIS. Environmental Management, 34 (2), 223-232.

Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic Regression Models. Landslides, 4 (33), 33-41.

Lee, S., Lee, M. J., & Jung, H. S. (2018). Spatial assessment of urban flood susceptibility using data mining and geographic information system (GIS) tools. Sustainability, 10 (3), 648.

Linusa, J., Tanjayaa, V., & Kurniawan, A. (2024). Performance Comparison of Machine Learning Methods for Flood Prediction, 9th International Conference on Computer Science and Computational Intelligence 2024 (ICCSCI 2024), Procedia Computer Science 245. 1040–1046.

Lyu, H., Zhou, W., Shen, S., & Zhou, N. (2020). Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen. Sustainable Cities and Society, 56, https://doi.org/10.1016/j.scs.2020.102103.

Mahdavi, M. (2023). Applied hydrology, 1st edition. Tehran: Tehran University Press.

Massey, A., Douglas, S., & Denton, A. (1988). The Dimensions of Residential”. Social Forces, 67 (2), 281–315.

Mekanik, F., Imteaz, M., Gato-Trinidad, S., & Elmahdi, A. (2013). Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes. J. Hydrol, 503, 11–21.

Mohammadi, H., & Panahi, A. (2006). Estimation of runoff using SCS and GIS in Ghale Chay watershed (East azarbaijan province), Iranian journal of geographical association. New Period of 10 and 11 Autumn and Winter, 109-123.

Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological. and biological applications. Hydrol. Process, doi: 10.1002/hyp.3360050103.

Mosavi, A., Rabczuk, T., & Varkonyi-Koczy, A. R. (2017). Reviewing the novel machine learning tools for materials design. Int. Conf. Glob. Res. Educat, 50–58.

Nachappa, T. G., Piralilou, S. T., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., & Blaschke, T. (2020). Flood susceptibility mappingwith machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology, 590, 125275.

Nguyen, V. N., Bui, D. T., Ngo, P. T., Nguyen, Q. P., Nguyen, V. C., Long, N. Q., & Revhaug, I. (2017). An integration of least squares support vector machines and firefly optimization algorithm for flood susceptible modeling using GIS. Presented at the International Conference on GeoSpatial Technologies and Earth Resources, Springer: 52–64.

Obada, E., Biaoa, E. I., Zohouc, P. J., Yaroud, H., Hounnondahoa, F. Z., & Alamoua, E. A. (2025). Using machine learning and satellite data to improve flood forecasting: the case of the Ouémé basin at the Bétérou outlet. Hydrology Research, 56, 2, 153, doi: 10.2166/nh.2025.133.

Panahi, M., Dodangeh, E., Rezaie, F., Khosravi, K., Van Le, H., Lee, M. J., & Thai Pham, B. (2021). Flood spatial prediction modeling using a hybrid of metaoptimization and support vector regression modeling. Catena, 199,105114.

Pavesi, L., Volpi, E., & Fiori, A. (2024). Flood risk assessment through large-scale modeling under uncertainty, Nat. Hazards Earth Syst. Sci, 24, 4507–4522, 2024, https://doi.org/10.5194/nhess-24-4507-2024.

Polykretis, C., & Chalkias, C. (2018). Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Nat Hazards, 93, 249–274. https://doi.org/ 10.1007/s11069-018-3299-7

Pradhan, B. (2010). Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol, 9(2),1–18.

Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., & Holland, G. J. (2017). The future intensification of hourly precipitationextremes. Nature Climate Change, 7(1), 48–52.

Razavi Termeh, S.V., & Malek, M. R. (2017). Preparation of flood susceptibility mapping using an ensemble of frequency ratio and adaptive neuro-fuzzy inference system models (Case study: Jahrom Township). Journal of Geomatics Science and Technology, 8(3), 1-15.

Rozalis, S., Morin, E., Yair, Y., & Price, C. (2010). Flash floodprediction using an uncalibrated hydrological model andradar rainfall data in a Mediterranean watershed underGEOLOGY, ECOLOGY, AND LANDSCAPES 17changing hydrological conditions. Journal of Hydrology, 394 (1–2), 245–255. https://doi.org/10.1016/j.jhydrol.2010.03.021.

Rudari, R., Silvestro, F., Campo, L., Rebora, N., Boni, G., & Herold, C. (2015). Improvement of the Global Flood Model for the GAR 2015. Geneva, United Nations.

Sahana, M., Rehman, S., Sajjad, H., & Hong, H. (2020). Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India. Catena, 189, 104450.

Samanta, S., Pal, D. K., & Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8, 66.

Sampson, C. C., Smith, A. M., Bates, P. B., Neal, J. C., Alfieri, L., & Freer, J. E. (2015). A high-resolution global flood hazard model. Water Resour. Res, 51, 7358–7381. doi: 10.1002/2015WR016954.

Santos, P. P., Reis, E., Pereira, S., & Santos, M. (2019). A flood susceptibility model at the national scale based on multicriteria analysis. Science of The Total Environment, 667, 325-337, https://doi.org/10.1016/j.scitotenv.2019.02.328.

Schumann, G. J. P., Stampoulis, D., Smith, A. M., Sampson, C. C., Andreadis, K. M., & Neal, J. C (2016). Rethinking flood hazard at the global scale. Geophys. Res. Lett, 43, 249–256, doi: 10.1002/2016GL070260.

Seydi, S. T., Kanani-Sadat, Y., Hasanlou, M., Sahraei, R., Chanussot, J., & Amani, M. (2023). Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping. Remote Sensing, 15 (1), 192. https://doi.org/10.3390/rs15010192.

Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J. J., Geertsema, M., Khosravi K., Amini, A., Bahrami, S., & Rahmati, O. (2020). Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens, 12 (2), 266.

Shatnawi, A., & Ibrahim, M. (2022). Derivation of flood hydrographs using SCS synthetic unit hydrograph technique for Housha catchment area. Water Supply, 22, 4888 doi: 10.2166/ws.2022.169.

Shirzadi, A., Saro, L., Joo, O. H., & Chapi, k. (2012). A gis based logistic regression model in roak fall susepitbility mapping along a mountainous  road : salavat abad case study, Kurdistan , iran. Natural hazard, 64, 1639- 1656.

Soma, A. S., Kubota, T., & Mizuno, H. (2019). Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia. J Mt Sci, 16, 144–162. https://doi.org/10.1007/s11629-018-4884-7.

Sulaiman, J., & Wahab, S. H. (2017). Heavy rainfall forecasting model using artificial neural network for flood prone area. Lecture Notes Electr Eng, 68–76. doi: 10.1007/978-981-10-6451-7_9.

Tabari, H. (2020). Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports, 10 (1), 1–10.

Taheri, M., & Ahmad, L. (2012). Application of Remote Sensing and GIS in Preparing Land Cover and Land Use Information Layers for MPSIAC Erosion Model, First National Conference on Strategies for Achieving Sustainable Development (Agriculture, Natural Resources and Environment), Tehran.

Tang, Z., Zhang, H., Yi, S., & Xiao, Y. (2018). Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. Journal of Hydrology, 558, 144-158, https://doi.org/10.1016/j.jhydrol.2018.01.033.

Tehrany, M. S., & Kumar, L. (2018). application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environmental Earth Sciences, 77, 490.

Tehrany, M. S., Kumar, L., & Shabani, F. (2019). A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia. PeerJ 7: e7653.

Tehrany, M. S., Kumar, L., Neamah Jebur, M., & Shabani, F. (2019). Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Haz Risk, 10 (1), 79–101.

Tien Bui, D., Bui, Q.T., Nguyen, Q.P., Pradhan, B., Nampak, H., & Trinh, P.T. (2017). A hybrid artificial
intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for
forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32-44.

Upretia, P., & Ojha, C. (2022). Development and performance evaluation of SCS-CN based hybrid model. Water Science & Technology. 85. 2479 doi: 10.2166/wst.2022.145.

Wang, L. J., Guo, M., Sawada, K., Lin, J., & Zhang, J. (2015). Landslide susceptibility mapping in MizunamiCity, Japan: A comparison between logistic regressionbivariate statistical analysis and multivariate adaptiveRegression spline models. Catena, 135, 271-282.

Wang, Y., Fang, Z., Hong, H., & Peng, L. (2020). Flood susceptibility mapping using convolutional neural network frameworks. J. Hydrol, 582, 124482.

Wang, Y., Hong, H., Chen, W., Li, S., Pamučar, D., Gigović, L., Drobnjak, S., Tien Bui, D., & Duan, H. (2019.) A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sens, 11 (1), 62.

Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S., & Costache, R. (2019). Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. J. Environ. Manage, 247, 712–729.

Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., & Bouwman, A. (2013). A framework for global river flood risk assessments. Hydrol. Earth Syst. Sci, 17, 1871–1892. doi: 10.5194/hess-17-1871-2013.

Wubalem, A., Tesfaw, G., Dawit, Z., Getahun, B., Mekuria, T., & Jothimani, M. (2021). Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia. Open Geosciences, 13, 1668–1688.

Xie, H., Dong, J., Shen, Z., Chen, L., Lai, X., Qiu, J., & Chen, X. (2019). Intra-and inter-event characteristics and controlling factors of agricultural nonpoint source pollution under different types of rainfall-runoff events. Catena, 182,104105.

Xu, Q., Shi, Y., Bamber, L., Ouyang, Ch., & Xiang Zhu, X. (2024). Large-scale flood modeling and forecasting with FloodCast, Water Research, 264, 122162. https://doi.org/10.1016/j.watres.2024.122162

Yamazaki, D., Kanae, S., Kim, H., & Oki, T. (2011). A physically based description
of floodplain inundation dynamics in a global river routing model. Water Resour. Res, 47:W04501. doi: 10.1029/2010WR009726.

Zhao, G., Pang, B., Xu, Z., Yue, J., & Tu, T. (2018). Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ, 615,1133–42.

Zhao, R. J., Liu, X. R., & Singh, V. P. (1995). The Xinanjiang model. Comput. Models Watershed Hydrol 135, 371–381.