منابع
ذرتی پور، امین.، شهپریفر، قاسم.، یوسفی، اشکان. (1404). مدلسازی تغییرات پوشش جنگلهای استان خوزستان مبتنی بر الگوریتم یادگیری ماشین و تصاویر ماهواره. تحقیقات آب و خاک ایران. 56(8)، 2057-2077.
doi:10.22059/ijswr.2025.394576.669934
References
Aksoy, S., Sertel, E., Roscher, R., Tanik, A., & Hamzehpour, N. (2024). Assessment of soil salinity using explainable machine learning methods and Landsat 8 images. International
Journal of Applied Earth Observation and Geoinformation, 130, 103879.
doi: 10.016/j.jag.2024.103879.
Buba, F. N., Gajere, E. N., & Ngum, F. F. (2020). Assessing the Correlation between Forest Degradation and Climate Variability in the Oluwa Forest Reserve, Ondo State, Nigeria. American Journal of Climate Change, 9(4), 371-390. doi: 10.4236/ajcc.2020.94023.
Dias, F., Suhadolnik, N., Camargo, H., & Da Silva, S. (2024). Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics. Journal of Environmental Management, 362,121359. doi:10.1016/j.jenvman.2024.121359.
Duwal, S., Liu, D., Pradhan, P.M., (2023). Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches. Geomatics, Nat. Hazards Risk 14 (1), 2217321.
doi:10.1080/19475705.2023.2217321.
Fischer, S., and
Schumann, H. (2025). Dominant flood types in Europe and their role in flood statistics.
Hydrological Sciences Journal. 994-1010. doi:10.1080/02626667.2025.2450369.
Halder, S., Bose, S., 2024. Comparative study on remote sensing-based indices for urban ecology assessment: A case study of 12 urban centers in the metropolitan area of eastern India.
Journal of Earth System Science. 133: 100.
doi:10.1007/s12040-024-02321-3.
Hu, K.H., Zhang, X., You, Y., Hu, X.D., Liu, W.M., Li, Y., 2019. Landslides and dammed lakes 805 triggered by the 2017 Ms6.9 Milin earthquake in the Tsangpo gorge.
Landslides 16(5), 993- 806 1001.
doi:10.1007/s10346-019-01168-w.
Kumar, B. P., Babu, K. R., Anusha, B. N., & Rajasekhar, M. (2022). Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI/TIRS, LST, and NDVI approach.
Environmental Challenges, 8, 100578.
doi: 10.1016/j.envc.2022.100578.
Liu, W.M., Carling, P.A., Hu, K.H., Wang, H., Zhou, Z., Zhou, L.Q., Liu, D.Z., Lai, Z.P., Zhang, 829 X.B., 2019. Outburst floods in China: A review.
Earth Sci. Rev. 197, 102895.
doi: 10.1016/j.earscirev.2019.102895.
Maharjan, M., Timilsina, S., Ayer, S., Singh, B., Manandhar, B., & Sedhain, A. (2024). Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal.
Natural Hazards Research. 4:24-35.
doi: 10.1016/j.nhers.2024.01.001.
Nur, I., Mondal, P., (2023). GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India.
Natural Hazards Research. 3:420-436.
doi: 10.1016/j.nhres.2023.05.004.
Pavandmehr, P., Zoratipour, A., Moazami, M., & Cheraghi, M. (2025). Flood vulnerability zoning is based on remote sensing indices and decision-making techniques (Case study: Rakat basin of Khuzestan). Water and Soil Management and Modeling, 5(2), 234-250. doi:10.22098/mmws. 2024.15911.1501.[In Persian]
Rahimikhoob, A. (2014). Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment.
Water Resources Management, 28, 657-669.
http://jstnar.iut.ac.ir/article-1-2499-fa. [In Persian].
Saha, S., Saha, M., Mukherjee, K., Arabameri, A., Ngo, P. T., T., & Paul, G. C. (2020). Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India.
Science of the Total Environment, 730, 139197.
doi: 10.1016/j.scitotenv.2020.139197.
Shabani, S., Pourghasemi, H. R., & Blaschke, T. (2020). Forest stand susceptibility mapping during harvesting using logistic regression and boosted regression tree machine learning models.
Global Ecology and Conservation, 22, e00974.
doi: 10.1016/j.gecco. 2020.e00974.
Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications.
World Information Technology and Engineering Journal, 10(07), 3897-3904.
doi:10.1016/j.world.tec.eng.2023.e10.3897-3904.
Wang, Z., Wang, Y., Liu, Y., Wang, F., Deng, W., & Rao, P. (2023). Spatiotemporal characteristics and natural forces of grassland NDVI changes in Qilian Mountains from a sub-basin perspective.
Ecological Indicators, 157, 111186.
doi: 10.1016/j.ecolind.2023.111186.
Zhang, B., He, X., Ouyang, F., Gu, D., Dong, Y., Zhang, L., ... & Zhang, S. (2017). Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.
Cancer Letters, 403, 21-27.
doi: 10.1016/j.canlet.2017.06.004.
Zoratipour, A., Shahparifar, Q. and Yousefi, A. (2025). Modeling changes in forest cover in Khuzestan province based on machine learning algorithm and satellite images.
Iranian Soil and Water Research. 56(8), 2057-2077.
doi:10.22059/ijswr.2025. 394576. 669934. [In Persian]
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