پهنه‌بندی خطر وقوع زمین‌لغزش با استفاده از تکنیک مدل‌سازی MLR (مطالعه موردی: حوضه آبریز ده‌شیخ)

اصغری سراسکانرود، صیاد؛ پالیزبان، دلنیا؛ امامی، هادی و قلعه، احسان  (1399). تحلیل مدل‌های تحلیل شبکه و منطق فازی برای تهیه نقشه پهنه‌بندی حساسیت وقوع زمین‌لغزش مطالعه موردی: (جاده سراب-نیر). جغرافیا و برنامه‌ریزی، 24(73), 1-22. Doi: 10.22034/gp.2020.10792

انتظاری، مژگان؛ استکی، شکوه و غلام حیدری، حمیده (1403). بررسی وضعیت زمین‌لغزش حوضه آبریز طارم با استفاده از مدل همپوشانی ریسک-آسیب پذیری. جغرافیا و برنامه‌ریزی، 28(89)، 61-39. Doi: 10.22034/gp.2023.54607.3073.

ذاکری‌نژاد، رضا و عموشاه، ناهید (1401). ارزیابی خطر زمین‎لغزش با استفاده از داده های سنجش از دور و مدل حداکثر آنتروپی (منطقه مورد مطالعه: حوضه آبخیز کمه، جنوب استان اصفهان). پژوهش‌های ژئومورفولوژی کمی، 11(2)، 128-149. Doi: 10.22034/gmpj.2022.340900.1349

ساداتی، سیدحمید؛ موسوی، سیدرمضان؛ وهاب‌زاده کبریا، قربان و روشان، سیدحسین (1403). ارزیابی مدل‌های جنگل تصادفی و ماشین بردار پشتیبان در تهیه نقشه حساسیت زمین‌لغزش (مطالعه موردی: حوضه تجن، استان مازندران). مخاطرات محیط طبیعی، (انتشار آنلاین)، 1-1. Doi: 10.22111/jneh.2025.50031.2071

سپه‎وند، علیرضا و بیرانوند، نسرین (1403). پهنه‌بندی حساسیت وقوع زمین‌لغزش با استفاده از الگوریتم‌های یادگیری ماشین (منطقة مورد مطالعه: بخشی از حوزة آبخیز هراز). مدل‎سازی و مدیریت آب و خاک، 4(2)، 261-278. Doi: 10.22098/mmws.2023.12678.1263.

شریفی‌پیچون، محمّد؛ شیرانی، کوروش و شیرانی، مائده (1400). اولویت‏بندی عوامل مؤثر بر وقوع زمین‌لغزش و پهنه‌بندی حساسیت آن با استفاده از روش رگرسیون چندمتغیره‌ی خطی مطالعه ی موردی: حوضه ی آبریز وهرگان-غرب استان اصفهان. هیدروژئومورفولوژی، 8(26)، 163-139. Doi: 10.22034/hyd.2021.12902.

صدیقی، حدیثه و قاسمی، احمد رضا (1402). مدل‌سازی خطر وقوع زمین لغزش با استفاده از مدل رگرسیون لجستیک (مطالعه موردی: استان چهار محال و بختیاری). پژوهش‎های دانش زمین، 14(4)، 42-60. Doi: 10.48308/esrj.2023.104053.

محمدی، مجتبی؛ عفیفی، محمد ابراهیم و قنبری، عبدالرسول (1402). پهنه‌بندی خطر زمین لغزش با استفاده از سیستم استنتاج فازی در حوضه‌ی رودخانه‌ی ایذه، علوم جغرافیایی، (19)42، 179-156.

نظریانی، نسترن، و فلاح اصغر (1402). مدل‌سازی خطر وقوع زمین‌لغزش با استفاده از داده‌کاوی در جنگل‌های هیرکانی، پ‍‍ژوهشنامه مدیریت حوضه آبخیز، ۱۴۰۲: ۱۴ (۲۷) :۱۳۴-۱۲۳ .

Abbasov, R., Fahs, M., Younes, A., Nowamooz, H., Maloy, K. J., & Toussaint, R. (2024). Modeling rainfall-induced landslide using the concept of local factor of safety: Uncertainty propagation and sensitivity analysis. Computers and Geotechnics, 167, 106102.

Agboola, G., Hashemi Beni, L., Elbayoumi, T., & Thompson, G. (2024). Optimizing landslide susceptibility mapping using machine learning and geospatial techniques.  Ecological Informatics, 81 (2024) 102583. https://doi.org/10.1016/j.ecoinf.2024.102583.

Ahyuni, A., Rizki, A. H., Endah, P., & Yurni, S. (2022). Random Forest Method Utilization For Landslide Hazard Zonation In Lima Puluh Kota Regency. ICGEO 2022, November 19-20. DOI 10.4108/eai.19-11-2022.2332288.

Asghari, S., Palizban, D., Emami, H., & Ghaleh, E. (2020). Evaluation of Fuzzy Logic and Network Analysis Models for Mapping Landslide Sensitivity Case Study: (Sarab - Nir Road). Journal of Geography and Planning, 24(73), 1-22. doi: 10.22034/gp.2020.10792 [In Persian]

Chen, Z., Tang, J., & Song, D. (2024). Modeling landslide susceptibility using alternating decision tree and support vector. Terrestrial, Atmospheric and Oceanic Sciences, 35(1), 12. https://doi.org/10.1007/s44195-024-00074-6

Dahal, A., Castro-Cruz, D. A., Tanyaş, H., Fadel, I., Mai, P. M., van der Meijde, M., van Westen, C., Huser, R., & Lombardo, L. (2023). From ground motion simulations tolandslide occurrence prediction. Geomorphology 441, 108898. https://doi.org/10.1016/j.geomorph.2023.108898.

Dahal, A., Huser, R., Lombardo, L.,( 2024). At the junction between deep learning andstatistics of extremes: formalizing the landslide hazard definition. arXiv preprintarXiv:2401.14210. https://doi.org/10.48550/arXiv.2401.14210.

Dahal, A., Lombardo, L.,( 2023). Explainable artificial intelligence in geoscience: Aglimpse into the future of landslide susceptibility modeling. Computer Geoscience. 176, 105364. https://doi.org/10.1016/j.cageo.2023.105364.

Dahal, A., Tanyaş, H., Lombardo, L.,(2024). Full seismic waveform analysis com-bined with transformer neural networks improves coseismic landslide prediction.Commun. Environmental Earth Sciences. 5 (1), 75. https://doi.org/10.1038/s43247-024-01243-8.

Dahal, A., Tanyas, H., van Westen, C., van der Meijde, M., Mai, P. M., Huser, R., & Lombardo, L. (2024). Space–time landslide hazard modeling via Ensemble Neural Networks, Nat. Natural Hazards and Earth System Sciences., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, 2024.

Dastranj, A., Hamzeh, N., & Bagherian Kalat, A. (2022). GIS-based LandslideSusceptibility Zoning Using Multi-criteria Decision-Making Method: A Case Study in BinaloodMountains, Iran. Quarterly Scientific Journal of Rescue and Relief, 2022; 14(1): 19-29. Doi: 10.32592/jorar.2022.14.1.3.

Dey, S., & Das, S. (2025). Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification. Earth Science Informatics, 18, 145. https://doi.org/10.1007/s12145-024-01617-8

Emiloglu, A., Zhu, L., Mohammednour, A. B., Azarafza, M., & Nanehkaran, Y. A. (2023). LandslideSusceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm. Land. 2023; 12(7):1397. https://doi.org/10.3390/land12071397.

Entezari, M. , Esteki, S. and Gholamhaydari, H. (2024). Investigation of State of Landslide in Tarom Watershed Using Risk-Vulnerability Superimposed Model. Journal of Geography and Planning, 28(89), 61-39. doi: 10.22034/gp.2023.54607.3073. [In Persian]

Feng, L., Zhang, M., Mao, Y., Chen, X., Wang, J., & Liu, H. (2025). Convolutional neural network-based deep learning for landslide susceptibility mapping in the Bakhtegan watershed. Scientific Reports, 15, 13250. https://doi.org/10.1038/s41598-025-96748-3

Halder, K., Srivastava, A. K., Ghosh, A. (2025). Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework. Scientific Reports, 15, 5170. https://doi.org/10.1038/s41598-025-87587-3

Karam, A., & Turani, M., (2013). Zoning of Land Susceptibility to Landslide Occurrence Using Linear Regression Methods and Analytic Hierarchy Process Case Study: Haraz Axis from Roudehen to Rineh. Applied Research in Geographical Sciences (Geographical Sciences), 13(28), 177-190. url:http://jgs.khu.ac.ir//artic-1-691. [In Persian]

Krkac, M., Sanja, B., & Gazibara, S. (2021). model of the slow-moving Kostanjek landslide in Zagreb, Croatia. Rudarsko-geološko-naftni zbornik 36(2):59-68.DOI:10.17794/rgn.2021.2.6.

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, 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(20), https://doi.org/10.1016/j.ecolind.2024.111600.

Mohammadi, M., Afifi, M. E., & Ghanbari, A. (2023). Landslide hazard zoning using a fuzzy inference system in the Izeh River basin, Geographical Sciences, 19(42), 156-179. [In Persian]

Nazariani N, Fallah A. (2023). Landslide Risk Modeling using Data Mining in Hyrcanian Forests. Watershed Management Research. 14(27), 123-134. doi:10.61186/jwmr.14.27.123. [In Persian]

Niraj, K. C., Ankit, S., & Dericks Praise, S. (2023). Effect of the Normalized Difference Vegetation Index (NDVI) on GIS-Enabled Bivariate and Multivariate Statistical Models for Landslide Susceptibility Mapping. Journal of the Indian Society of Remote Sensing 51(3).DOI:10.1007/s12524-023-01738-5.

Niraj, A. K. C. Singh and D. P. Shukla, (2023). Improved Landslide Susceptibility mapping using statistical MLR model, International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), Hyderabad, India, 1-4, doi: 10.1109/MIGARS57353.2023.10064594.

Ramani, S. (2017). Anempirical study to identify factors causing landslides using multiple linear regression model (MLR) January 2017. Disaster Advances, 10(9):18-26.

Rihan, M., Talukdar, S., Naikoo, M. W. (2024). Improving landslide susceptibility prediction in Uttarakhand through hyper-tuned artificial intelligence and global sensitivity analysis. Earth Systems and Environment. 15:13250. https://doi.org/10.1007/s41748-024-00457-2

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

Rohan, E., Shelef, B., Mirus, T., & Coleman, T. (2023). Prolonged influence of urbanization on landslide susceptibility. Landslides, 20(5), 1023-1035. https://doi.org/10.1007/s10346-023-02034-6

Sabri, M., Ahmad, F., & Samui, P. (2024). Slope stability analysis of heavy-haul freight corridor using novel machine learning approach. Modeling Earth Systems and Environment, 10(1), 201–219. https://doi.org/10.1007/s40808-023-01774-7

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, 1-1. doi: 10.22111/jneh.2025.50031.2071. [In Persian]

Seddighi, H. and Ghasemi, A. R. (2023). Landslide risk modeling using logistics regression model (Case study: Chaharmahal and Bakhtiari province). Researches in Earth Sciences, 14(4), 42-60. doi: 10.48308/esrj.2023.104053. [In Persian]

Sepahvand, A. & Beiranvand, N. (2024). Landslide susceptibility mapping using various soft computing techniques (Case study: A part of Haraz Watershed). Water and Soil Management and Modelling, 4(2), 261-278. doi: 10.22098/mmws.2023.12678.1263. [In Persian]

Sharifi Paichoon, M. , Shirani, K. and Shirani, M. (2021). Prioritization of Factors Affecting the Occurrence of Landslides and Zoning Its Sensitivity Using Multiple Linear Regression Case Study: Vahargan Catchment-west of Isfahan Province. Hydrogeomorphology, 8(26), 163-139. doi: 10.22034/hyd.2021.12902. [In Persian]

Tian, F., Wei, Z., Zhang, H., Cui, W., Chen, F., Li, H., & Tan, D. (2025). Multi-temporal InSAR-based landslide dynamic susceptibility mapping of Fengjie County, Three Gorges Reservoir Area, China, Journal of Rock Mechanics and Geotechnical Engineering, 1674-7755, https://doi.org/10.1016/j.jrmge.2025.01.012.

Wang, Y., Sun, D., Wen, H., Zhang, H., & Zhang, F. (2020). Comparison of random forest model and frequency ratio model for landslide susceptibility mapping (LSM) in Yunyang County (Chongqing, China). International journal of environmental research and public health, 17(12), 4206. doi.org/10.3390/ijerph17124206.

Yadav, R., Huser, R., Opitz, T., & Lombardo, L. (2023). Joint modelling of landslidecounts and sizes using spatial marked point processes with sub-asymptotic markdistributions. Royal Statistical Society. Stat. qlad077. https://doi.org/10.48550/arXiv.2205.09908.

Yaghoubi, E., Yaghoubi, E., Khamees, A. (2024). A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering. Neural Comput & Applic, 36, 12655–12699. https://doi.org/10.­1007/­s00521-024-09893-7

Yang, Z., Cheng, D., & Xia, J. (2024). Effects of the Rainfall-Triggered Lisse Effect on the Stability of Loess Slopes. Journal of Earth Science, 35, 1254–1262. https://doi.org/10.1007/s12583-021-1536-6

Ye, C., Wu, H., Oguchi, T., Tang, Y., Pei, X., & Wu, Y. (2025). Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges. Remote Sensing, 17(13), 2280. https://doi.org/10.3390/rs17132280

Zakerinejad, R., & Amoshahi, N. (2022). Assessment of Landslide Hazard Using Remote sensing data and the Maximum Entropy Model (Case Study: Kome watershed, in south of Isfahan Province). Quantitative Geomorphological Research, 11(2), 128-149. doi: 10.22034/gmpj.­2022.­340900.1349. [In Persian]

Zhang, Y.; Wu, W.; Qin, Y.; Lin, Z.; Zhang, G.; Chen, R.; Song, Y.; Lang, T.; Zhou, X.; Huangfu, W.; (2020). Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China. Geo-Information, 9, 695. https://doi.org/10.3390/ijgi9110695.