- Adnan, R.M., Liang, Z., & Heddam, S. (2019). Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, (586), 124371. https://doi.org/10.1016/J.JHYDROL.2019.124371
- Atkinson, P., Jiskoot, H., Massari, R., & Murray, T. (1998). Generalized linear modelling in geomorphology. Earth Surface Processes and Landforms. The Journal of the British Geomorphological Group, 23(13), 1185-1195.
- Avand, M., & Moradi, H. (2021). Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. Journal of Hydrology, 595, 125663. https://doi.org/10.1016/j.jhydrol.2020.125663
- Avand, M. T., Moradi, M., Ramazanzadeh Lasboyee, Mehdi. (2021). Spatial prediction of future flood risk: an approach to the effects of climate change. Geosciences, 11(1), 1-20. https://doi.org/10.3390/geosciences11010025
- Avand, M.T., Moradi, H.R., & Ramzanzadeh Lesboi, M. (2020). Preparation of flood sensitivity map using Bayesian random forest and linear generalized machine learning models. Environment and Water Engineering, 6(1), 83-95.
- Avand, M., Kuriqi, A., Khazaei, M., & Ghorbanzadeh, O. (2022). DEM resolution effects on machine learning performance for flood probability mapping. Journal of Hydro-Environment Research, 40, 1-16. https://doi.org/10.1016/j.jher.2021.10.002
- Azadi, F., Sadouq, S.H., Ghahrodi, M., & Shahabi, H. (2020). Flood risk sensitivity zoning in Kashkan watershed using WOE and EBF models. Scientific Journal of Geography and Environmental Hazards, 9(1), 45-60.
- Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Bui, D.T., Pham, B.T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95, 229-245. https://doi.org/10.1016/j.envsoft.2017.06.012
- Hasanuzzaman, M., Islam, A., Bera, B., & Shit, P.K. (2022). A comparison of performance measures of three machine learning algorithms for flood susceptibility mapping of river Silabati (tropical river, India). Physics and Chemistry of the Earth, Parts A/B/C,127, 103198. https://doi.org/10.1016/j.pce.2022.103198
- Kalantar, B., Ueda, N., Saeidi, V., Ahmadi, K., Halin, A. A., & Shabani, F. (2020). Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data. Remote Sensing, 12(11), 1737. https://doi.org/10.3390/rs12111737
- Khosravi, K., Nohani, E., Maroufinia, E., Pourghasemi, H.R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83, 947–987. https://doi.org/10.1007/s11069-016-2357-2
- 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, 212–225. https://doi.org/10.1080/02626667.2011.555836
- Lazarus, E.D., Constantine, J.A. (2013). Generic theory for channel sinuosity. Proceedings of the National Academy of Sciences USA, 110, 8447–8452. https://doi.org/10.1073/pnas.1214074110
- Marmion, M., Luoto, M., Heikkinen, R.K., & Thuiller, W. (2009). The performance of state-of-the-art modelling techniques depends on geographical distribution of species. Ecological Modelling, 220(24), 3512-3520. https://doi.org/10.1016/j.ecolmodel.2008.10.019
- Mosavi, A., Golshan, M., Janizadeh, S., Choubin, B., Melesse, A.M., & Dineva, A.A. (2020). Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto International, 37(9): 1-20. https://doi.org/10.1080/10106049.2020.1829101
- Mirchooli, F., Motevalli, A., Pourghasemi, H.R., Mohammadi, M., Bhattacharya, P., Maghsood, F.F., & Tiefenbacher, J.P. (2019). How do data-mining models consider arsenic contamination in sediments and variables importance? Environmental Monitoring and Assessment, 191. https://doi.org/10.1007/s10661-019-7979-x
- Mohammadi, M., Darabi, H., Mirchooli, F., Bakhshaee, A., & Torabi Haghighi, A. (2021). Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed, northern Iran. Natural Hazards, 105, 2007-2025. https://doi.org/10.1007/s11069-020-04387-w
- Nachappa, T.G., Ghorbanzadeh, O., & Gholamnia, K. (2020). Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sensing, 12(17), 2757. https://doi.org/10.3390/rs12172757
- Mosavi, A., Golshan, M., Janizadeh, S., Choubin, B., Melesse, A.M., & Dineva, A.A. (2022). Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto International, 37(9), 2541-2560. https://doi.org/10.1080/10106049.2020.1829101
- Pourghasemi, H.R., & Beheshtirad, M. (2015). Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto International, 30, 662-685. https://doi.org/10.1080/10106049.2014.966161
- Pourghasemi, H.R., Kariminejad, N., Amiri, M., Edalat, M., Zarafshar, M., Blaschke, T., & Cerda, A. (2020). Assessing and mapping multi- hazard risk susceptibility using a machine learning technique. Scientific Reports, 10, 1–11. https://doi.org/10.1038/s41598-020-60191-3
- 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. https://doi.org/10.1080/10106049.2015.1041559
- Rutkowski, L., Jaworski, M., Pietruczuk, L., & Duda, P., (2014). The CART decision tree for mining data streams. Information Sciences, 266, 1–15. https://doi.org/10.1016/j.ins.2013.12.060
- Seydi, S. T., Kanani-Sadat, Y., Hasanlou, M., Sahraei, R., Chanussot, J., & Amani, M. (2022). Comparison of machine learning algorithms for flood susceptibility mapping. Remote Sensing, 15(1), 192
- Skilodimou, H.D., Bathrellos, G.D., Chousianitis, K., Youssef, A.M., Pradhan, B. (2019). Multi-hazard assessment modeling via multi-criteria analysis and GIS : a case study. Environmental Earth Sciences, 78, 42. https://doi.org/10.1007/s12665-018-8003-4
- Tehrany, M.S., Pradhan, B., & Jebur, M.N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, 29, 1149–1165. https://doi.org/10.1007/s00477-015-1021-9
- Tehrany, M.S., Pradhan, B., & Jebur, M.N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
- Tehrany Shafapour, M., Pradhan, B., Mansor, S.H., & Noordin, A. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91-101.
- Yousefi, H., Yonsei, H. A., Davoudi-Moghadam, D., Arshiya, A., & Shamsi, Z. (2022). Determination of flood potential using CART, GLM and GAM machine learning models (case study: Kashkan Basin). Scientific Research Journal of Irrigation and Water Engineering of Iran, 12(48), 84-105.
- Yousefi, H., Yonsei, H.A., Arshiya, A., Yarahamdi, Y., & Guderzi, A. (2021). Determining flood-prone areas with models to reduce flood risks (case study: Kashkan watershed). Ecohydrology, 8(1), 307-319.
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