ارزیابی حساسیت‌پذیری زمین‌لغزش در بخشی از شمال شهر تهران

Aditian, A., Kubota, T., Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318, 101–111.

Aghayary, L., Asghari Saraskanrood, S., Zeynali, B. (2024). Identification and zoning of landslide prone areas in Germi city. Hydrogeomorphology 11(39), 1-18.

Alsabhan, A.H., Singh, K., Sharma, A., Alam, S., Pandey, D.D., Rahman, S.A.S., Khursheed, A., & Munshi, F.M. (2022). Landslide susceptibility assessment in the Himalayan range based along Kasauli–Parwanoo road corridor using weight of evidence, information value, and frequency ratio. Journal of King Saud University - Science, 34(2), 101759.

Asmare, D. (2023). Application and validation of AHP and FR methods for landslide susceptibility mapping around choke mountain, northwestern Ethiopia. Scientific African 19, e01470.

Ayalew, L., Yamagishi, H. (2005). The Application of GIS – based logistic regression for landslide susceptibility mapping in the Kakuda–Yahiko Mountains, central Japan. Geomorphology,  65, 15-31.

Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P.M., & Derakhshani, R. (2021). Deep learning-based landslide susceptibility mapping, Scientific Reports 11 (1): 24112. https://doi.org/10.1038/s41598-021-03585-1..

Bisht, S., Rawat, K.S., & Singh, S.K. (2024). Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India. Quaternary Science Advances. 13,100141.

Chang, Z., Catani, F., Huang, F., Liu, G., Meena, S., Huang, J., & Zhou, C. (2023). Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors, J. Rock Mech. Geotech. Eng. 15 (5),1127–1143.

Chen, W., & Li, Y. (2020). GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena 195, 104777. https://doi.org/10.1016/j. catena.2020.104777.

Chowdhury, S., Rahman, N., Sheikh, S., Sayeid, A., Mahmud, K.H., & Hafsa, B. (2024). GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh. Heliyon 10, e23424. https://doi.org/10.1016/j.heliyon.2023.e23424.

Ercanoglu, M., & Gokceoglu, C. (2002). Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ. Geol 41 (6), 720–730.

Esfandyari Darabad, F., Rostami, G., Mostafazadeh, R., Abedini, M. (2024). Spatial assessment and zoning of landslide risk in Zamkan watershed using support vector machine and logistic regression. Hydrogeomorphology, 11(40), 102-123.

Fang, Z,, Wang, Y., Peng, L., & Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifers for landslide susceptibility mapping. Comput. Geosci 139: 104470 https://doi.org/10.1016/j.cageo.2020.104470.

Fatemi Aghda, S.M., Bagheri, V., & Razifard, M. (2018). Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region, Tehran, Iran. Geotechnical and Geological Engineering, 36, 915–937.

Froude, M., & Petley, D. (2018). Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci 18, 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018.

Ghaedi Vanani, A.A., Shoaei, M., & Zare, M. (2024). Landslide Susceptibility Mapping in North Tehran, Iran: Linear Regression, Neural Networks, and Fuzzy Logic Approaches. Geotechnical and Geological Engineering 42: 7159–7186. https://doi.org/10.1007/s10706-024-02920-3.

Guo, Z., Guo, F., Zhang, U., He, J. Li, G., Yang, Y., & Zhang, X. (2023). A python system for regional landslide susceptibility assessment by integrating machine learning models and its application. Heliyon. 9, e21542.

Hunter, G., & Fell, R. (2003). Travel distance angle for “rapid” landslides in constructed and natural soil slopes. Can. Geotech. J 40: 1123–1141. https://doi.org/10.1139/t03-061.

Kamp, U., Growley, B.J., Khattak, G.A., & Owen, L.A. (2008). GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101 (4): 631–642.

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.

Liu, X.D., Xiao, T., Zhang, S.H., Sun, P.H., Liu, L.L., & Peng. Z.W. (2024). Comparative study of sampling strategies for machine learning-based landslide susceptibility assessment. Stoch Env Res Risk Assess 38,4935–4957.

Lombardo, L., Martin, & Mai. P. (2018). Presenting logistic regression-based landslide susceptibility results". Eng. Geol 244, 14–24.

Martinello, C., Delchiaro, M., Iacobucci, G., Cappadonia, C., Rotigliano, E., & Piacentini, D. (2024) Exploring the geomorphological adequacy of the landslide susceptibility maps: A test for different types of landslides in the Bidente river basin (northern Italy). Caena 238, 107835. https://doi.org/10.1016/j.catena.2024.107835.

Masruroh, H., Irawan, L.Y.C., & Anam, C. (2024). Comprehensive spatial analysis landslide susceptibility modelling, spatial cluster analysis and priority zoning for environment analysis. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-024-05950-9.

Mohammady, M., & Davudirad, A. (2024). Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran. Environmental Modeling & Assessment. 29,249–261.

Mohammady, M., Pourghasemi, H.R., Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences. 61,221–236.

Nefeslioglu, H.A., Duman, T.Y., & Durmaz, S. (2008). Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Easten Black Sea Region of Turkey). Geomorphology. 94,401-418.

Pourghasemi, H.R., Mohammady, M., Pradhan, B. (2012). Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97, 71–84.

Pourghasemi, H.R., Moradi, H.R., Fatemi Aghda, S.M., Gokceoglu, C., Pradhan, B. (2014). GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci. 7, 1857–1878.

Rai, S.C., Pandey, V.K., Sharma, K.K., Sharma, S. (2024). Landslide susceptibility analysis in the Bhilangana Basin (India) using GIS-based machine learning methods. Geosystems and Geoenvironment 3, 100253. https://doi.org/10.1016/j.geogeo.2024.100253.

Rasyid, A.R., Bhandary, N.P., Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain. Indonesia Geoenviron Disasters. 3,19. https:// doi. org/ 10. 1186/ s40677- 016- 0053.

Roback, K., Clark, M.K., West, A.J., Zekkos, D., Li, G., Gallen, S.F., Chamlagain, D., & Godt, J.W. (2017). The size, distribution, and mobility of landslides caused by the 2015 M w 7.8 Gorkha earthquake Nepal. Geomorphology 301: 121–138. https://doi.org/10.1016/j.geomorph.2017.01.030.

Saha, S., Majumdar, P., & Bera, B. (2023). Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India). 10: 100075. https://doi.org/10.1016/j.qsa.2023.100075.

Sharma, N., Saharia, M., & Ramana, G.V. (2024). High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data. Catena 235, 107653. https://doi.org/10.1016/j.catena.2023.107653.

Wang, L.Q., Xiao, T., Liu, S.L., Zhang, W.G., Yang, B.B., & Chen, L.C. (2023). Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation. Gondwana Res. 123, 27-40. https://doi.org/10.1016/j .gr.2023.03.006.

Wang, Y., Kang, L., & Wang, J. (2024). Landslide risk assessment combining kernel extreme learning machine and information value modeling-A case study of Jiaxian Country of loess plateau, China. Heliyon 10, e37352. https://doi.org/10.1016/j.heliyon.2024.e37352.

Wubalem, A., Getahun, B., Hailemariam, Y., Mesele, A., Tesfaw, G., Dawit, Z., & Goshe, E. (2022). Landslide susceptibility modeling using the index of entropy and frequency ratio method from nefas-mewcha to weldiya road corridor, northwestern Ethiopia. Geotech. Geol. Eng 40 (10), 5249–5278.

Xi, C. Han, M. Hu, X. Liu, B. He, K. Luo, G. & Cao, X. (2022). Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression, Bull. Eng. Geol. Environ. 81 (5), 1–21.

Yuvaraj, R.M., & Dolui, B. (2023).  Geographical assessment of landslide susceptibility using statistical approach. Quaternary Science Advances 11, 100097. https://doi.org/10.1016/j.qsa.2023.100097.

Zhang, A., Zhao, X.W., Zhao, X.Y., Zheng, X.Z., Zeng, M., Huang, X., Wu, P., Jiang, T., Wang, S.C., He, J., & Li, Y.Y. (2024). Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China. China Geology 7, 104−115.