حیدری، ناصر؛ حبیبنژاد، محمود؛ کاویان، عطااله؛ پورقاسمی، حمیدرضا (1399). مدلسازی حساسیت زمینلغزش با الگوریتم یادگیری ماشین جنگل تصادفی در آبخیز سد رئیسعلی دلواری، پژوهشهای آبخیزداری (پژوهش و سازندگی)، دوره 33، شماره 1 (پیاپی 126)، صص 2-13.
سدیدی، جواد؛ مالکی، رضا (1401). مقایسه الگوریتمهای ماشین بردار پشتیبان، جنگل تصادفی و رگرسیون لجستیک در پهنهبندی خطر زمینلغزش در جاده مهاباد-سردشت، سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی، دوره 2، شماره 4، صص 81-100.
https://doi:10.22034/rsgi.2022.15839
ساداتی، سیدحمید؛ موسوی، سیدرمضان؛ وهابزاده کبریا، قربان؛ روشان، سیدحسین (1404). ارزیابی مدلهای جنگل تصادفی و ماشین بردار پشتیبان در تهیه نقشه حساسیت زمینلغزش (مطالعه موردی: حوضه تجن، استان مازندران)، مخاطرات محیط طبیعی، دوره 14، شماره 45، صص 133-154.
https://doi.org/10.22111/jneh.2025.50031.2071
قیاسی، واحد؛ شیرخانی چشمه شفیع، یوسف؛ یوسفی، مهیار (1401). مروری بر مطالعات انجام شده در زمینه پهنهبندی خطر زمینلغزش (الگوریتم جنگل تصادفی)، رویکردهای نوین در مهندسی عمران، دوره 6، شماره 4، صص 1-10.
کرم، امیر؛ پاکنژاد، فریبا؛ بهرامآبادی، الهام (1400). پهنهبندی ناپایداری دامنهها نسبت به حرکات واریزهای با استفاده از روش الگوریتم جنگل تصادفی (مطالعه موردی: حوضه تنگراه-استان گلستان)، پژوهشهای ژئومورفولوژی کمّی، دوره 9، شماره 4، صص 59-74. https://doi:10.22034/gmpj.2021.131011
داوریصارم، میلاد؛ حسنپور، جعفر؛ روشن لیارجدمه، مائده (1404). شناسایی پهنههای احتمالی زمینلغزش با استفاده از دادههای DInSAR و استعداد زمینلغزش: مطالعه موردی حوضه آبریز دماوند، مخاطرات محیط طبیعی، دوره 14، شماره 45، صص 53-74. https://doi.org/10.22111/jneh.2025.49171.2058
ذاکرینژاد، رضا؛ کهرانی، عباس (1402). ارزیابی و مقایسه مدلهای CART و TreeNet جهت تهیه نقشههای حساسیت زمینلغزش با استفاده از نرمافزار SPM و سامانه اطلاعات جغرافیایی (GIS) (مطالعه موردی: حوضه آبخیز کمه، جنوب استان اصفهان)، مخاطرات محیط طبیعی، دوره 12، شماره 37، صص 17-38. https://doi.org/10.22111/jneh.2023.42304.1904
محرمی، میثم؛ جلوخانی نیارکی، محمدرضا (1402). تهیه نقشه مناطق مستعد وقوع زمینلغزش با استفاده از مدلهای آماری و یادگیری ماشین (مطالعه موردی: کشور اتریش)، علوم و فنون نقشهبرداری، دوره 13، شماره 2، صص 79-94.
مومیپور، مهدی؛ معاوی، مهشید (1401). تحلیل شرایط تکتونیک و فرسایش در حوضه سد شهید عباسپور با تکنیکهای ژئومورفومتری، جغرافیا و مخاطرات محیطی، دوره 11، شماره 1، صص 1-16. doi:10.22067/geoeh.2021.70140.1053
Achour, Y., & Pourghasemi, H. R. (2020). How do machine learning techniques help in increasing the accuracy of landslide susceptibility maps? Geoscience Frontiers, 11(3), 871-883. https://doi.org/10.1016/j.gsf.2019.10.001
Agboola, G., Hashemi Beni, L., Elbayoumi, T., & Thompson, G. (2024). Optimizing landslide susceptibility mapping using machine learning and geospatial techniques.
Ecological Informatics, 81, 102583.
https://doi.org/10.1016/j.ecoinf.2024.102583
Ahmad, M. N., Shao, Z., Aslam, R. W., Ahmad, I., Liao, M., Li, X., & Song, Y. (2022). Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: A case study of Muzaffarabad, Pakistan. Stochastic Environmental Research and Risk Assessment, 36(8), 1-16. https://doi.org/10.1007/s00477-022-02245-8
Ahyuni, R., Atthoriq, H., Endah, P., & Yurni, S. I. (2023). Hazard zonation in Lima Puluh Kota Regency. In
Proceedings of the 5th International Conference on Geoscience and Earth Resources Engineering (pp. 45-58). EAI.
https://doi.org/10.4108/eai.19-11-2022.2332288
Cheng, Y.-S., Yu, T.-T., & Son, N.-T. (2021). Random forests for landslide prediction in Tsengwen River watershed, Central Taiwan.
Remote Sensing, 13(2), 199.
https://doi.org/10.3390/rs13020199
Dao, A., Jaafari, M., Bayat, D., Mafi-Gholami, C., Qi, H., Moayedi, T. V., Thanh, B. T. (2020). A spatially explicit deep learning neural network model for the prediction of landslide susceptibility.
Catena, 188, 104451.
https://doi.org/10.1016/j.catena.2019.104451
Davari Sarem, M., Hassanpour, J. and Roshan Liarajdameh, M. (2025). Identification of potential landslide zones using DInSAR data and landslide susceptibility: a case study of the Damavand basin. Journal of Natural Environmental Hazards, 14(45), 53-74. doi: 10.22111/jneh.2025.49171.2058. [In Persian].
Egbueri, J. C., & Egbueri, F. (2023). Soil erosion and landslide susceptibility insights based on hierarchical clustering and multilayer perceptron networks: A Nigerian case study.
International Journal of Environmental Science and Technology, 20(10), 10763-10786.
https://doi.org/10.1007/s13762-022-04714-7
Fan, G., Scaringi, G., Korup, O., West, A. J., van Westen, C. J., Tanyas, H., Huang, R. (2019). Earthquake-induced chains of geologic hazards: Patterns, mechanisms, and impacts.
Reviews of Geophysics, 57(2), 421-503.
https://doi.org/10.1029/2018RG000626
Karam, A., paknejad, F. and bahram abadi, E. (2021). Zonation of unstable slopes with respect to the debris flows using random forest algorithm (case study: Basin Tngrah Golestan Province). Quantitative Geomorphological Research, 9(4), 59-74. doi: 10.22034/gmpj.2021.131011[In Persian]
Gu, T., Li, J., Wang, M., Duan, P., Zhang, Y., & Cheng, L. (2023). Study on landslide susceptibility mapping with different factor screening methods and random forest models.
PLoS ONE, 18(10), e0292897.
https://doi.org/10.1371/journal.pone.0292897
Ghiyasi, Vahed; Shirkhani Cheshme Shafi, Yousef; Yousefi, Mahyar (2012). A review of studies conducted in the field of landslide hazard zoning (random forest algorithm), New Approaches in Civil Engineering, Volume 6, Issue 4, pp. 1-10. [In Persian]
Haque, U., Da Silva, P. F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., Wu, J. H. (2019). The human cost of global warming: Deadly landslides and their triggers (1995-2014).
Science of The Total Environment, 682, 673-684.
https://doi.org/10.1016/j.scitotenv.2019.03.415.
Heydari, N., Habibnejad, M., Kavian, A. and Pourghasemi, H. R. (2020). Landslide Susceptibility Modelling Using the Random Forest Machine Learning Algorithm in the Watershed of Rais-Ali Delvari Reservoir. Watershed Management Research, 33(1), 2-13. doi: 10.22092/wmej.2019.126288.1219 [In Persian]
He, Y., Zhao, Z., Zhu, Q., Liu, T., Zhang, Q., Yang, W., Wang, Q. (2024). An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features.
International Journal of Digital Earth, 17(1), 2295408.
https://doi.org/10.1080/17538947.2023.2295408
Li, M., Wang, H., Chen, J., & Zheng, K. (2024). Assessing landslide susceptibility based on the random forest model and multi-source heterogeneous data.
Ecological Indicators, 158, 111600.
https://doi.org/10.1016/j.ecolind.2024.111600
Li, Z., Huang, L., Fan, L., Huang, J., Huang, F., Chen, J., Wang, Y. (2020). Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network.
Sensors, 20(6), 1576.
https://doi.org/10.3390/s20061576
Liu, Q., Pan, M., Wang, X., & An, Y. (2021). A two-layer model for landslide-generated impulse wave: Simulation of the 1958 Lituya Bay landslide impact wave from generation to long-duration transport.
Advances in Water Resources, 154, 103989.
https://doi.org/10.1016/j.advwatres.2021.103989
Lu, J., Ren, C., Yue, W., Zhou, Y., Xue, X., Liu, Y., & Ding, C. (2023). Investigation of landslide susceptibility decision mechanisms in different ensemble-based machine learning models with various types of factor data.
Sustainability, 15(18), 13563.
https://doi.org/10.3390/su151813563
Lu, S., Tang, H., Zhang, Y., Gong, W., & Wang, L. (2018). Effects of the particle-size distribution on the micro and macro behavior of soils: Fractal dimension as an indicator of the spatial variability of a slip zone in a landslide.
Bulletin of Engineering Geology and the Environment, 77(2), 665-677.
https://doi.org/10.1007/s10064-017-1028-1
Moharrami, Meysam; Jellokhani-Niarkai, Mohammad Reza (2013). Mapping landslide-prone areas using statistical models and machine learning (case study: Austria), Surveying Sciences and Techniques, Volume 13, Issue 2, pp. 79-94. [In Persian]
Mumipour, M. and Moavi, M. (2022). Tectonic and Erosion Analysis of Shahid Abbasspour Dam Catchment using Geomorphometric Techniques. Journal of Geography and Environmental Hazards, 11(1), 1-16. doi: 10.22067/geoeh.2021.70140.1053. [In Persian]
Nwazelibe, E., Nwazelibe, C. O., & Unigwe, J. C. (2023). Integration and comparison of algorithmic weight of evidence and logistic regression in landslide susceptibility mapping of the Orumba North erosion-prone region, Nigeria.
Modeling Earth Systems and Environment, 9(1), 967-986.
https://doi.org/10.1007/s40808-022-01549-2
Panahi, A., Gayen, A., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms.
Science of The Total Environment, 741, 139937.
https://doi.org/10.1016/j.scitotenv.2020.139937
Panchal, A., & Shrivastava, N. (2022). Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India.
Ain Shams Engineering Journal, 13(5), 101626.
https://doi.org/10.1016/j.asej.2021.10.021
Pourghasemi, H. R., Kornejady, A., Kerle, N., & Shabani, F. (2020). Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping.
Catena, 187, 104364.
https://doi.org/10.1016/j.catena.2019.104364
Rahaman, A., Dondapati, S., Gupta, S., & Raj, R. (2024). Leveraging artificial neural networks for robust landslide susceptibility mapping: A geospatial modeling approach in the ecologically sensitive Nilgiri District, Tamil Nadu.
Geohazard Mechanics, 2(4), 258-269.
https://doi.org/10.1016/j.ghm.2024.07.001
Rodrigues Neto, J. M., & Bhandary, N. P. (2024). Landslide susceptibility assessment by machine learning and frequency ratio methods using XRAIN radar-acquired rainfall data.
Geosciences, 14(6), 171.
https://doi.org/10.3390/geosciences14060171
Sadati, S. H., Mousavi, S. R., Vahabzadeh Kebria, G. and Roshun, S. H. (2025). Evaluation of Random Forest and Support Vector Machine Models in Landslide Risk Mapping (Case study: Tajan Basin, Mazandaran Province). Journal of Natural Environmental Hazards, 14(45), 133-154. doi: 10.22111/jneh.2025.50031.2071. [In Persian]
Sadidi, J. and Maliki, R. (2022). Using machine learning-based models for landslide susceptibility mapping in the Mahabad-Sardasht road. Remote Sensing and GIS Applications in Environmental Sciences, 2(4), 100-81. doi: 10.22034/rsgi.2022.15839. [In Persian]
Shugar, D. H., Sharma, M., Saharia, G. V., & Ramana, P. V. (2021). High-resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data.
Catena, 235, 107653.
https://doi.org/10.1016/j.catena.2024.107653
Viet, D. T., Doan, A. T., & Tran, A. I. (2023). Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam.
Geocarto International, 38(1), 1-25.
https://doi.org/10.1080/10106049.2023.2198856
Wu, Z., Li, H., Yuan, S., Gong, Q., Wang, J., & Zhang, B. (2023). Mask R-CNN-based landslide hazard identification for 22.6 extreme rainfall-induced landslides in the Beijiang River Basin, China.
Remote Sensing, 15(20), 4898.
https://doi.org/10.3390/rs15204898
Wubalem, A. (2021). Landslide susceptibility mapping using statistical methods in the Uatzau catchment area, northwestern Ethiopia.
Geoenvironmental Disasters, 8(1), 1-21.
https://doi.org/10.1186/s40677-021-00187-x
Xiao, T., Yin, K., Yao, T., & Liu, S. (2019). Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China.
Acta Geochimica, 38(4), 654-669.
https://doi.org/10.1007/s11631-019-00341-1
Xing, Y., Yang, C., Wang, P., & Huang, S. (2024). Research on landslide master control factor identification and susceptibility prediction modelling.
EGUsphere, 2023, 1-25.
https://doi.org/10.5194/egusphere-2023-3134
Youssef, A. M., & Pourghasemi, H. R. (2021). Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia.
Geoscience Frontiers, 12(2), 639-655.
https://doi.org/10.1016/j.gsf.2020.05.010
zakeri nejad, R. , Kahrani, A. and Entrzari, M. (2023). Assessment of gully erosion susceptibility using the CART model and GIS (Case study: Khasyeh watershed). Quantitative Geomorphological Research, 12(2), 72-90. doi: 10.22034/gmpj.2023.391714.1429.[In Persian]
Zhao, Z., Liu, Z. Y., & Xu, C. (2021). Slope unit-based landslide susceptibility mapping using certainty factor, support vector machine, random forest, CF-SVM and CF-RF models.
Frontiers in Earth Science, 9, 589630.
https://doi.org/10.3389/feart.2021.589630
Zuo, R., & Carranza, E. J. M. (2017). A fractal measure of spatial association between landslides and conditioning factors.
Journal of Earth Science, 28(4), 588-594.
https://doi.org/10.1007/s12583-017-0738-4.
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