ارزیابی قابلیت مدل تحلیل تفکیک‌کننده انعطاف‌پذیر در پیش‌بینی استعداد سیل‌گیری حوزه آبخیز زرینه‌رود

Abedi, R., Costache, R., Shafizadeh-Moghadam, H., & Pham, Q.B. (2022). Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International, 37(19), 5479-5496. doi:10.1080/10106049.2021.1920636

Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223-1232. doi:10.1111/j.1365-2664.2006.01214.x

Arabameri, A., Seyed Danesh, A., Santosh, M., Cerda, A., Chandra Pal, S., Ghorbanzadeh, O., & Chowdhuri, I. (2022). Flood susceptibility mapping using meta-heuristic algorithms. Geomatics, Natural Hazards and Risk, 13(1), 949-974. doi:10.1080/19475705.2022.2060138

Arora, A., Pandey, M., Siddiqui, M.A., Hong, H., & Mishra, V.N. (2021). Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto International, 36(18), 2085-2116. doi:10.1080/10106049.2019.1687594

Barati, Gh., Bodagh Jamali, J., & Maleki, N. (2012). Anticyclones and heavy rainfalls over Western Iran. Physical Geography Research Quarterly, 44(2), 85-98. doi:10.22059/jphgr.2012.29208. [In Persian]

Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Tien Bui, D., Pham, B.T., & Khosravi. K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95, 229-245. doi:10.1016/j.envsoft.2017.06.012

Derex, M. (2022). Human cumulative culture and the exploitation of natural phenomena. Philosophical Transactions of the Royal Society B, 377(1843), 20200311. doi:10.1098/rstb.2020.0311

Habibi, A., Delavar, M.R., Sadeghian, M.S., & Nazari, B. (2023). Flood susceptibility mapping and assessment using regularized random forest and NAÏVE bayes algorithms. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 241-248. doi:10.5194/isprs-annals-X-4-W1-2022-241-2023

Hallgren, W., Santana, F., Low-Choy, S., Zhao, Y., & Mackey, B. (2019). Species distribution models can be highly sensitive to algorithm configuration. Ecological Modelling, 408, 108719. doi:10.1016/j.ecolmodel.2019.108719

Hastie, T., Tibshirani, R., Friedman, J.H., & Friedman, J.H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer. doi:10.1007/978-0-387-21606-5

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 of flood susceptibility map of Poyang County, China. Science of the Total Environment, 625, 575-588. doi:10.1016/j.scitotenv.2017.12.256

Karim, F., Armin, M.A., Ahmedt-Aristizabal, D., Tychsen-Smith, L., & Petersson, L. (2023). A review of hydrodynamic and machine learning approaches for flood inundation modeling. Water, 15(3), 566. doi:10.3390/w15030566

Kazemi, M., & Jafarpoor, A. (2022). Identifying the threshold of variables affecting flood zone using machine learning technique (Case study: Karun basin). Water and Soil Management and Modeling,  doi:10.22098/mmws.2023.12285.1220. [In Persian]

Moazzam, M.F.U., Lee, B.G., Rahman, A.U., Farid, N., & Rahman, G. (2020). Spatio-statistical analysis of flood susceptibility assessment using bivariate model in the floodplain of river swat, district charsadda, Pakistan. Journal of Geoscience and Environment Protection, 8(5), 159. doi:10.4236/gep.2020.85010

Newson, M., Lewin, J., & Raven, P. (2022). River science and flood risk management policy in England. Progress in Physical Geography: Earth and Environment, 46(1), 105-123. doi:0.1177/03091333211036384

Pontius Jr, R.G., & Schneider, L.C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1-3), 239-248. doi:10.1016/S0167-8809(01)00187-6

Pourghasemi, H.R., Pouyan, S., Bordbar, M., Golkar, F., & Clague, J.J. (2023). Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination. Natural Hazards, 1-20. doi:10.1007/s11069-023-05836-y

Prasad, P., Loveson, V.J., Das, B., & Kotha, M. (2022). Novel ensemble machine learning models in flood susceptibility mapping. Geocarto International, 37(16), 4571-4593. doi:10.1080/10106049.2021.1892209

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(1), 42-70. doi:10.1080/10106049.2015.1041559

Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H.R., & Feizizadeh, B. (2017). Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 298, 118-137. doi: 10.1016/j.geomorph.2017.09.006

Rajabizadeh, Y., Ayyoubzadeh, S.A., & Zahiri, A. (2019). Flood survey of Golestan Province in 2018-2019 and providing solutions for its control and management in the future. Ecohydrology, 6(4), 921-942 doi:10.22059/ije.2019.283004.1137. [in Persian]

Samanta, S., Pal, D.K., & Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8(2), 66. doi:10.1007/s13201-018-0710-1

Seleem, O., Ayzel, G., de Souza, A.C.T., Bronstert, A., & Heistermann, M. (2022). Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany. Geomatics, Natural Hazards and Risk, 13(1), 1640-1662. doi:10.1080/19475705.2022.2097131

Shafapour Tehrany, M., & Kumar, L. (2018). The 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, 1-24. doi:10.1007/s12665-018-7667-0

Shafapour Tehrany, M., Shabani, F., Neamah Jebur, M., Hong, H., Chen, W., & Xie, X. (2017). GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk, 8(2), 1538-1561. doi:10.1080/19475705.2017.1362038

Tajbakhsh, S.M., & Chezgi, J. (2022). Prioritization of flooding sub-basins in the north of the Birjand Plain using morphometric factors and VIKOR model. Water and Soil Management and Modeling doi:10.22098/mmws.2022.11855.1179. [In Persian]

Vafaei, M., Dastorani, M.T., & Rostami Khalaj, M. (2023). Flood risk assessment in campus of Ferdowsi University of Mashhad and presentation management scenarios using HEC-RAS model. Water and Soil Management and Modeling, 3(3), 225-239. doi:10.22098/mmws.2022.11815.1173. [In Persian]

Wubalem, A., Tesfaw, G., Dawit, Z., Getahun, B., Mekuria, T., & Jothimani, M. (2020). Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: in a case study of Tana sub-basin in northwestern Ethiopia. Natural Hazards and Earth System Sciences Discussions, 1-43. doi:10.1515/geo-2020-0329

Youssef, A.M., Pourghasemi, H.R., & El-Haddad, B. A. (2022). Advanced machine learning algorithms for flood susceptibility modeling—performance comparison: Red Sea, Egypt. Environmental Science and Pollution Research, 29(44), 66768-66792. doi: 10.1007/s11356-022-20213-1

Youssef, A.M., Pourghasemi, H.R., Mahdi, A.M., & Matar, S.S. (2023). Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models. Environmental Science and Pollution Research, 30(6), 16081-16105. doi:10.1007/s11356-022-23140-3