References (in Persian)
Afifi, M., (2019), Flood Hazards Susceptibility Map and its Occurrence Probability using Shannon Entropy Model (Case Study: Firoozabad River Basin). Environmental Management Hazards, 6(2), 149-167. DOI: 10.22059/JHSCI.2019.279717.462. [In Persian]
Feyzi, Z., Keshtkar, A., Malekian, A., Ghasemieh, H., (2016), Fuzzy AHP Application for Flood Spreading Site Selection (Case Study: South of Kashan Plain). Journal of Water and Soil Science; 20 (76):129-141. URL: http://jstnar.iut.ac.ir/article-1-3339-fa.html.[In Persian]
Ghavami, Z., Mohamadinia, A., (2017), Spatial forecasting of flood-prone areas using Geographic Information System (GIS). 4th national conference on Application of GIS in Water and Electric Industries, ARAK-2017- 13 & 14 Dec.
https://civilica.com/doc/711268. [In Persian]
Khosravi, K., Marufinia, E., Nohani, E., Chapy, K., (2017), Evaluation of Logistic Regression Efficiency in Mapping Flood Susceptibility. Journal of Range and Watershed Management, 69(4), 863-876. DOI: 10.22059/JRWM.2017.61187. [In Persian]
Mahmoudzadeh, H., Bakoi, M., (2018), Flood zoning using fuzzy analysis (case study: Sari city). Journal of Natural Environmental Hazards, 7(18), 51-68. DOI: 10.22111/JNEH.2018.19885.1238. [In Persian]
Roustaei, Sh., Mousavi, R., Alizade Gorji, GH., (2018), Watershed Flood Zoning Map Preparation Using CN and GIS/RS Methods: A Case Study on Nekarood. Quantitative Geomorphological Research, 6(1), 108-118. http://www.geomorphologyjournal.ir/article_78078.html. [In Persian]
Sharifi Paichoon, M., Omidvar, K., Motazaker, K., (2019), Assessment of flooding using cluster analysis and multivariable regression methods with emphasis on hydro geomorphological parameters (Case study: Maroon catchment). Journal of Natural Environmental Hazards, 8(21), 75-92. DOI: 10.22111/JNEH.2018.22519.1336. [In Persian]
References (in English)
Abed, K.A., Ahmad, A.A., (2020), The best parameters selection using PSO algorithm to solving for ito system by new iterative technique. Indonesian Journal of Electrical Engineering and Computer Science, 18(3), pp.1638-1645. https://doi.org/10.1103/PhysRevD.90.112016
Aghbashlo, M., Tabatabaei, M., Nadian, M.H., Davoodnia, V. and Soltanian, S., (2019), Prognostication of lignocellulosic biomass pyrolysis behavior using ANFIS model tuned by PSO algorithm. Fuel, 253, pp.189-198. https://doi.org/10.1016/j.fuel.2019.04.169
Alam, A., Ahmed, B., Sammonds, P., (2020), Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Quaternary International. https://doi.org/10.1016/j.quaint.2020.04.047
Ardiansyah, A., Sumunar, D.R.S., (2020), Flood Vulnerability Mapping Using Geographic Information System (GIS) in Gajah Wong Sub Watershed, Yogyakarta County Province. Geosfera Indonesia, 5(1), pp.47-64. https://doi.org/10.19184/geosi.v5i1.9959
Beheshti, Z., (2020), A time-varying mirrored S-shaped transfer function for binary particle swarm optimization. Information Sciences, 512, pp.1503-1542. https://doi.org/10.1016/j.ins.2019.10.029
Eini, M., Kaboli, H.S., Rashidian, M., Hedayat, H., (2020), Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts". International Journal of Disaster Risk Reduction, p.101687. https://doi.org/10.1016/j.ijdrr.2020.101687
Fotheringham, A.S., Oshan, T.M., )2016), Geographically weighted regression and multicollinearity: dispelling
the myth. Journal of Geographical Systems, 18(4), pp.303-329. https://doi.org/10.1007/s10109-016-0239-5
Guevara, J., Zadrozny, B., Buoro, A., Lu, L., Tolle, J., Limbeck, J., Wu, M. and Hohl, D., (2018), A hybrid data-driven and knowledge-driven methodology for estimating the effect of completion parameters on the cumulative production of horizontal wells. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/191446-MS
Hudson, P., Botzen, W.W., (2019), Cost-benefit analysis of flood‐zoning policies: A review of current practice. Wiley Interdisciplinary Reviews: Water, 6(6), p.e1387. https://doi.org/10.1002/wat2.1387
Hutahaean, S., (2019), Correlation of Weighting Coefficient at Weighted Total Acceleration with Rayleigh Distribution and with Pierson-Moskowitz Spectrum. International Journal of Advanced Engineering Research and Science, 6(3). DOI: 10.22161/ijaers.6.3.33
Jancewicz, K., Migoń, P. and Kasprzak, M., (2019), Connectivity patterns in contrasting types of tableland sandstone relief revealed by Topographic Wetness Index". Science of The Total Environment, 656, pp.1046-1062. https://doi.org/10.1016/j.scitotenv.2018.11.467
Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., Nasseri, M., (2019), A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi-criteria decision-making method". Journal of Hydrology, 572, pp.17-31. https://doi.org/10.1016/j.jhydrol.2019.02.034
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 a multi-criteria decision- making technique. Nat. Hazards 83 (2), 947–987. https://doi.org/10.1007/s11069-016-2357-2
Murray, A.T., Xu, J., Baik, J., Burtner, S., Cho, S., Noi, E., Pludow, B.A. and Zhou, E., (2020), Overview of Contributions in Geographical Analysis: Waldo Tobler". Geographical Analysis.
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J, Fotheringhm, A.S., (2019), MGWR: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale, ISPRS International Journal of Geo-Information, 8 (6), p. 269. https://doi.org/10.3390/ijgi8060269
Papaioannou, G., Vasiliades, L., Loukas, A., (2015), Multi-criteria analysis framework for potential flood-prone areas mapping. Water resources management, 29(2), 399-418. https://doi.org/10.1007/s11269-014-0817-6
Pourghasemi, H.R., Razavi-Termeh, S.V., Kariminejad, N., Hong, H. and Chen, W., (2020), An assessment of metaheuristic approaches for flood assessment. Journal of Hydrology, 582, p.124536. https://doi.org/10.1016/j.jhydrol.2019.124536
Saa-Requejo, A., Martin-Sotoca, J.J., Valencia, J.L., Rodriguez-Sinobas, L., Tarquis, A.M., (2019), Modified Fournier index as a new metric of integrated degradability index ". In Geophysical Research Abstracts (Vol. 21).
Shafapour, M., Shabanihttps, F., Neamah Jebur, M., Honghttps, H., Chenhttps, W., Xie, X, (2017), GIS-based spatial prediction of flood-prone areas using standalone frequency ratio, logistic regression, the weight of evidence and their ensemble techniques, Geomatics, Natural Hazards and Risk, 8:2, 1538-1561. DOI: 10.1080/19475705.2017.1362038
Vojtek, M., Vojteková, J., (2019), Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water, 11(2), p.364. https://doi.org/10.3390/w11020364
Wang, X., Liu, H., (2019), A Knowledge-and Data-Driven Soft Sensor Based on Deep Learning for Predicting the Deformation of an Air Preheater Rotor. IEEE Access, 7, pp.159651-159660. https://doi.org/10.1109/ACCESS.2019.2950661
Wu, D., (2020), Spatially and Temporally Varying Relationships between Ecological Footprint and Influencing Factors in China's Provinces Using Geographically Weighted Regression (GWR). Journal of Cleaner Production, p.121089. https://doi.org/10.1016/j.jclepro.2020.121089
Xiao, Y., Yi, S., Tang, Z., (2017), Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Sci. Total Environ, 599, 1034. https://doi.org/10.1016/j.scitotenv.2017.04.2.