ثقفیان، بهرام، فرازجو، حسن، سپهری ، عادل، نجفینژاد، علی، (1385). بررسی اثر تغییرات کاربری اراضی بر سیل خیزی حوزۀ آبریز سد گلستان، تحقیقات منابع آب ایران، سال دوم، شماره ١، صص 28-18.
رحیمی، داریوش، رحیمی داشلیبرون، یونس، (1393). بررسی تغییرات اقلیم و کاربری اراضی بر سیلاب در شمال ایران (حوضۀ مادرسو)، جغرافیا و برنامه ریزی محیطی، سال 27، شماره 1، 89-102.
رشیدیان، مجتبی، (1401). ارزیابی اثرات تغییر کاربری حال و آینده بر روی ریسک سیلاب، پایان نامۀ کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه خواجه نصیرالدین طوسی.
سلامتی هرمزی، وحید، امیدوار، کمال، کاوسی، رضا، حمزه نژاد. مجتبی، (1396). شناسایی و تحلیل همدیدی-دینامیکی الگوهای جوی سیلاب آبان 1394 در استانهای ایلام و لرستان. نیوار. , 27-9، (97-96)41، 27-9.
طهماسبی، قباد، (1400). مدیریت بحران مخاطرات شهری با تأکید بر خطر وقوع سیل(مطالعۀ موردی: شهر ایلام)، پایاننامۀ دکتری، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی.
عباسزاده تهرانی، نادیا، مخدوم فرخنده، مجید ، مهدوی، محمد، ( 1389). بررسی تأثیر تغییرات کاربری اراضی بر میزان دبی سیلابها با کاربرد فناوری سنجش از دور و سامانه اطلاعات جغرافیایی (GIS) منطقۀ موردمطالعۀ: حوزۀ آبریز رودخانه مادرسو (پارک ملی گلستان)، پژوهشهای محیط زیست، دورۀ :1، شماره:2 ، صص14-1.
لاهوتی نسب, سیده فانز، قاسمیه، هدی، (1403). کاربرد DEMATEL-AHP و SVM در شناسایی مناطق مستعد سیلاب (مطالعه موردی: حوزه آبخیز برزک کاشان). تحقیقات آب و خاک ایران، 55(10)، صص 1960-1939.
نیکپور، نورالله؛ ثروتی، محمدرضا، حسینزاده، محمدمهدی، دهبزرگی، مریم، (1393). بررسی ژئومورفولوژی (مورفوتکتونیک) بخش میانی تاقدیس کبیر کوه واقع در استان ایلام (از پشته اریشت تا امام زاده شاه محمد کوه نشین)، پایانامۀ کارشناسی ارشد، دانشگاه شهید بهشتی.
Adeyemi, A. B., & Komolafe, A. A. (2025). Flood hazard zones prediction using machine-learning-based geospatial approach in the lower Niger River basin, Nigeria
. Natural Hazards Research. 5(2), 399–412.
https://doi.org/10.1016/j.nhres.2025.01.002
Ahmad, I., Farooq, R., Ashraf, M.
et al. Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning.
Nat Hazards 121, 7839–7868 (2025).
https://doi.org/10.1007/s11069-025-07109-2
Ahmed, I. A., Talukdar, S., Shahfahad, Parvez, A. et al. (2022). Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms. Geocarto International, 37(26), 12238-12266.
Al-Juaidi, A. E., Nassar, A. M., & Al-Juaidi, O. E. (2018). Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab J Geosci, 11(24), 765. https://doi.org/10.1007/s12517-018-4095-0
Amadio, M., Mysiak, J., Carrera, L., & Koks, E. (2016). Improving flood damage assessment models in Italy. Natural Hazards, 82, 2075-2088. DOI 10.1007/s11069-016-2286-0
Andaryani, S., Nourani, V., Haghighi, A. T., & Keesstra, S. (2021). Integration of hard and soft supervised machine learning for flood susceptibility mapping
. Journal of Environmental Management, 291, 112731.
https://doi.org/10.1016/j.jenvman.2021.112731
Arora, A., Arabameri, A., Pandey, M,
et al. (2021). Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India.
Science of the Total Environment, 750, 141565.
https://doi.org/10.1016/j.scitotenv.2020.141565
Ayalew, T. B., & Krajewski, W. F. (2017). Effect of river network geometry on flood frequency: a tale of two watersheds in Iowa. Journal of Hydrologic Engineering, 22(8), 06017004.
Aydin, H. E., & Iban, M. C. (2023). Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with Shapley Additive exPlanations. Natural Hazards, 116(3), 2957-2991.
Band, S. S., Janizadeh, S., Chandra Pal, S., et al. (2020). Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing, 12(21), 3568.
Benkirane, M., Laftouhi, N. E., El Mansouri, B. et al., ... & Zamrane, Z. (2020). An approach for flood assessment by numerical modeling of extreme hydrological events in the Zat watershed (High Atlas, Morocco). Urban Water Journal, 17(5), 381-389.
Bera, A., Meraj, G., Kanga, S., et al. (2022). Vulnerability and risk assessment to climate change in Sagar Island, India. Water, 14(5), 823.
Bui, D. T., Pradhan, B., Nampak, H.,
et al. (2016). Hybrid artificial intelligence approach based on a neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS.
Journal of Hydrology,
540, 317-330.
https://doi.org/10.1016/j.jhydrol.2016.06.027
Bui, Q. T., Nguyen, Q. H., Nguyen, X. L.,
et al. (2020). Verification of novel integrations of swarm intelligence algorithms into deep learning neural networks for flood susceptibility mapping.
Journal of Hydrology, 581, 124379.
https://doi.org/10.1016/j.jhydrol.2019.124379
Campolo, M., Soldati, A., & Andreussi, P. (2003). Artificial neural network approach to flood forecasting in the River Arno. Hydrological Sciences Journal, 48(3), 381-398.
Chen, W., Li, Y., Xue, W.,
et al. (2020). Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.
Science of The Total Environment,
701, 134979.
https://doi.org/10.1016/j.scitotenv.2019.134979
Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2018). A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77, 647-664. https://doi.org/10.1007/s10064-017-1010-y
Choubin, B., Moradi, E., Golshan, M.,
et al. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines.
Science of the Total Environment, 651, 2087-2096.
https://doi.org/10.1016/j.scitotenv.2018.10.064
Costache, R., Pham, Q. B., Arabameri, A., et al. (2022). Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning. Geocarto International, 37(25), 8361-8393.
Debnath, J., Sahariah, D., Nath, N., et al. (2024). Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques. Modeling Earth Systems and Environment, 10(2), 2393-2419.
Desai, B., Maskrey, A., Peduzzi, P., et al. (2015). Making development sustainable: the future of disaster risk management, global assessment report on disaster risk reduction (UNISDR).
Dou, J., Yamagishi, H., Pourghasemi, H. R., et al.. (2015). An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards, 78, 1749-1776. https://doi.org/10.1007/s11069-015-1799-2
Duque, E. L., & Aquino, P. T. (2020). Anthropometric analysis in automotive manual transmission gearshift quality perception. In CTI SYMPOSIUM 2018: 17th International Congress and Expo 3-6 December 2018, Berlin, Germany (pp. 97-109). Springer Berlin Heidelberg.
Fayne, J. V., Bolten, J. D., Doyle, C. S., et al. (2017). Flood mapping in the lower Mekong River Basin using daily MODIS observations. International journal of remote sensing, 38(6), 1737-1757.
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
Islam, A. R. M. T., Talukdar, S., Mahato, S., et al. (2021). Flood susceptibility modelling using advanced ensemble machine learning models. Geoscience Frontiers, 12(3), 101075.
Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope–Evros region, Greece.
Science of the Total Environment, 538, 555-563.
https://doi.org/10.1016/j.scitotenv.2015.08.055
Khoirunisa, N., Ku, C. Y., & Liu, C. Y. (2021). A GIS-based artificial neural network model for flood susceptibility assessment. International Journal of Environmental Research and Public Health, 18(3), 1072.
Khosravi, K., Pham, B. T., Chapi, K.,
et al. (2018). A comparative assessment of decision tree algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.
Science of the Total Environment, 627, 744-755.
https://doi.org/10.1016/j.scitotenv.2018.01.266
Kia, M. B., Pirasteh, S., Pradhan, B. et al. (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
Kumar, A., Houze, R. A., Rasmussen, K. L., & Peters-Lidard, C. (2014). Simulation of a flash flooding storm at the steep edge of the Himalayas. Journal of Hydrometeorology, 15(1), 212-228.
Lee, M. J., Kang, J. E., & Jeon, S. (2012a, July). Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In
2012, IEEE International Geoscience and Remote Sensing Symposium (pp. 895-898). IEEE DOI:
10.1109/IGARSS.2012.6351414
Liu, J., Wang, J., Xiong, J.,
et al. (2021). Hybrid models incorporating bivariate statistics and machine learning methods for flash flood susceptibility assessment based on remote sensing datasets
. Remote Sensing, 13(23), 4945. DOI:
10.3390/rs13234945
Mahmood, S. H. (2024). Estimating Models and Evaluating their Efficiency under Multicollinearity in Multiple Linear Regression: A Comparative Study. Zanco Journal of Human Sciences, 28(5), 264-277.
Mateo-Garcia, G., Veitch-Michaelis, J., Smith, L., et al. (2021). Towards global flood mapping onboard low-cost satellites with machine learning. Scientific reports, 11(1), 7249.
Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article" Assessment of economic flood damage". Natural Hazards and Earth System Sciences, 10(8), 1697-1724.
Mishra, A., Mukherjee, S., Merz, B., Singh, V. P., Wright, D. B., Villarini, G., ... & Stedinger, J. R. (2022). An overview of flood concepts, challenges, and future directions. Journal of Hydrologic Engineering, 27(6), 03122001.
Mišić, V. V. (2020). Optimization of tree ensembles. Operations Research, 68(5), 1605-1624.
Mohajane, M., Essahlaoui, A. L. I., Oudija, F., ., et al. (2018). Land use/land cover (LULC) using Landsat data series (MSS, TM, ETM+, and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5(12), 131.
Mohammadi, A., Kamran, K. V., Karimzadeh, S.,
et al. (2020). Flood detection and susceptibility mapping using Sentinel-1 time series, alternating decision trees, and bag-adtree models. Complexity, 2020, 1-21.
https://doi.org/10.1155/2020/4271376
Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080-1102.
Nevo, S., Morin, E., Gerzi Rosenthal, A., et al. (2022). Flood forecasting with machine learning models in an operational framework. Hydrology and Earth System Sciences, 26(15), 4013-4032.
Opperman, J. J., Galloway, G. E., Fargione, J., et al. (2009). Sustainable floodplains through large-scale reconnection to rivers. Science, 326(5959), 1487-1488.
Phillips, T. H., Baker, M. E., Lautar, K.,
et al. (2019). The capacity of urban forest patches to infiltrate stormwater is influenced by soil physical properties and soil moisture.
Journal of Environmental Management, 246, 11-18.
https://doi.org/10.1016/j.jenvman.2019.05.127
Plate, E. J. (2002). Flood risk and flood management. Journal of Hydrology, 267(1-2), 2-11.
Pourali, S. H., Arrowsmith, C., Chrisman, N.,
et al. (2016). Topography wetness index application in flood-risk-based land use planning.
Applied Spatial Analysis and Policy, 9, 39-54.
https://doi.org/10.1007/s12061-014-9130-2
Rahman, M., Ningsheng, C., Mahmud, G. I., et al. (2021). Flooding and its relationship with land cover change, population growth, and road density. Geoscience Frontiers, 12(6), 101224.
Ren, H., Pang, B., Bai, P., ., et al. (2024). Flood susceptibility assessment with random sampling strategy in ensemble learning (RF and XGBoost). Remote Sensing, 16(2), 320.
Saber, M., Boulmaiz, T., Guermoui, M., et al. (2023). Enhancing flood risk assessment through integration of ensemble learning approaches and physically-based hydrological modeling. Geomatics, Natural Hazards and Risk, 14(1), 2203798.
Sahu, A. S., & Bengal, N. W. (2018). Detection of water-logged areas using geoinformatics techniques and relationship study in Panskura-Tamluk flood plain (India). Trans. Inst. Indian Geographers, 40(1), 9-24.
Salvati, A., Nia, A. M., Salajegheh, A., .,
et al. (2023). Flood susceptibility mapping using support vector regression and hyper‐parameter optimization.
Journal of Flood Risk Management, e12920.
https://doi.org/10.1111/jfr3.12920
Sarkar, D., & Mondal, P. (2020). Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region. Applied Water Science, 10(1), 1-13.
Seydi, S. T., Kanani-Sadat, Y., Hasanlou, M., et al. (2022). Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping. Remote Sensing, 15(1), 192.
Shirani, K., & Zakerinejad, R. (2021). Watershed prioritization for the identification of spatial hotspots of flood risk using the combined TOPSIS-GIS-based approach: a case study of the Jarahi-Zohre catchment in Southwest Iran. AUC GEOGRAPHICA, 56(1), 120-128.
Sugianto, S., Deli, A., Miswar, E., et al. (2022). The Effect of Land Use and Land Cover Changes on Flood Occurrence in Teunom Watershed, Aceh Jaya. Land, 11(8), 1271.
Teegavarapu, R. S. (2012). Floods in a changing climate: Extreme precipitation. Cambridge University Press.
Tripathi, P. (2015). Flood disaster in India: an analysis of trend and preparedness. Interdisciplinary Journal of Contemporary Research, 2(4), 91-98.
Uddin, K., Gurung, D. R., Giriraj, A., & Shrestha, B. (2013). Application of remote sensing and GIS for flood hazard management: a case study from Sindh Province, Pakistan. American Journal of Geographic Information Systems, 2(1), 1-5.
Verma, S., Bhatla, R., Shahi, N. K., & Mall, R. K. (2022). Regional modulating behavior of Indian summer monsoon rainfall in context of spatio-temporal variation of drought and flood events
. Atmospheric Research, 274, 106201.
https://doi.org/10.1016/j.atmosres.2022.106201
Vu, V. T., Nguyen, H. D., Vu, P. L.,
et al. (2023). Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam.
Water Practice and Technology.
https://doi.org/10.2166/wpt.2023.088
Wang, Y., Fang, Z., Hong, H., & Peng, L. (2020). Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology, 582, 124482.
Wang, Y., Fang, Z., Hong, H., .,
et al. (2021). Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.
Journal of Environmental Management, 289, 112449.
https://doi.org/10.1016/j.jhydrol.2019.124482
Wang, Y., Li, Z., Tang, Z., & Zeng, G. (2011). A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting Lake Region, Hunan, Central China.
Water resources management, 25, 3465-3484.
https://doi.org/10.1016/j.jenvman.2021.112449
Wang, Y., Zhang, P., Xie, Y.,
et al. (2025). Toward explainable flood risk prediction: Integrating a novel hybrid machine learning model.
Sustainable Cities and Society, 120, 106140.
https://doi.org/10.1016/j.scs.2025.106140
Winzeler, H. E., Owens, P. R., Read, Q. D., et al. (2022). Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land, 11(11), 2018.
Wright, S. (1921). Correlation and causation. Journal of agricultural research, 20(7), 557.
Xie, K., Ozbay, K., Zhu, Y., & Yang, H. (2017). Evacuation zone modeling under climate change: A data-driven method. Journal of Infrastructure Systems, 23(4), 04017013.
Yang, Y., & Li, X. (2022). Automatic Correction of Parameters of Rice Phenology Prediction Model Based on Random Forest Algorithm.
Procedia Computer Science, 208, 435-441.
https://doi.org/10.1016/j.procs.2022.10.061
Yousefi, S., Pourghasemi, H. R., Emami, S. N., ., et al. (2020). A machine learning framework for multi-hazard modeling and mapping in a mountainous area. Scientific Reports, 10(1), 12144.
Zanchetta, A. D., & Coulibaly, P. (2020). Recent advances in real-time pluvial flash flood forecasting. Water, 12(2), 570.
Zhang, J., & Chen, Y. (2019). Risk assessment of flood disaster induced by typhoon rainstorms in Guangdong province, China. Sustainability, 11(10), 2738.
Zhang, M., Fu, X., Liu, S., & Zhang, C. (2025). Integrating Remote Sensing and Machine Learning for Actionable Flood Risk Assessment: Multi-Scenario Projection in the Ili River Basin in China Under Climate Change. Remote Sensing, 17(7), 1189.