پهنه‌بندی حساسیت وقوع زمین‌لغزش با استفاده از الگوریتم‌های یادگیری ماشین (منطقة مورد مطالعه: بخشی از حوزة آبخیز هراز)

References

Ahmadabadi, A., & Rahmati, M. (2016). An application of quantitative geomorphometric indicators in identifying ‘[susceptible zones by using SVM model. Quantitative Geomorphological Research, 4(3),197-213. dor:20.1001.1.22519424.1394.4.3.14.1 [In Persian]

Ahmadi, H. (2007). Applied Geomorphology. 4th Edition:Tehran University Press, 688 pages. [In Persian]

Alijani, B., Ghahrodi Tali, M., & Amir Ahmadi, A. (2007). Landslide risk zoning in the northern slopes of Shahjahan using GIS. Geographical Research, 22(1), 117-132. [In Persian]

Caniani, D., Pascale, S., Sdao, F., & Sole A. (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Natural Hazards, 45, 55–72. doi:10.1007/s11069-007-9169-3

Chang, Z., Huang, F., Huang, J., Jiang, Sh.H., Yuting Liu, Y., Meena, S.R., & Catani, F. (2023). An updating of landslide susceptibility prediction from the perspective of space and time. Geoscience Frontiers, 14(5), 101619. doi:10.1016/j.gsf.2023.101619

Chen, J., Li, L., Xu, C., Huang, Y., Luo, Z., Xu, X., & Lyu, Y. (2023). Freely accessible inventory and spatial distribution of large-scale landslides in Xianyang City, Shaanxi Province, China. Earthquake Research Advances, 3(3), 100217. doi: 10.1016/j.eqrea.2023.100217.

Colkesen, I., Sahin, E.K., & Kavzoglu, T. (2016). Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. Journal of African Earth Sciences, 118, 53-64. doi:10.1016/j.jafrearsci.2016.02.019

Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning,  20(3), 273-297. doi: 10.1007/BF00994018.

Crosta, B.G. (2009). Dating, triggering, modelling and hazard assessment of large landslides. Geomorphology, 103(1), 1-4. doi:10.1016/j.geomorph.2008.04.007

Es-smairi, A., Elmoutchou, B., Mir, R.A., Touhami, A.E.O., & Namous, M. (2023). Delineation of landslide susceptible zones using Frequency Ratio (FR) and Shannon Entropy (SE) models in northern Rif, Morocco. Geosystems and Geoenvironment, 2(4), 100195. doi:10.1016/j.geogeo.2023.100195.

Ganesh, B., Vincent, Sh., Pathan, S., & Benitez, S.R.G. (2023). Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution. Remote Sensing Applications: Society and Environment, 29, 100905. doi:10.1016/j.rsase.2022. 100905.

Ghasemian, B., Abedini, M., Raushiti, Sh., & Shirzadi, A. (2018). Comparative study of support vector machine and tree logistic models for landslide susceptibility assessment. Natural Geography Quarterly, 10(39), 47-68. [In Persian]

Gill, M.K., Asefa, T., Kemblowski, M.W., & McKee, M. (2006). Soil moisture prediction using support vector machines. Journal of the American Water Resources Association, 42(4), 1033-1046. doi:10.1111/j.1752-1688.2006.tb04512.x

Goli-Mokhtar, L., & Naimi Tabar, M. (2022). Spatial modeling and prediction of landslide hazard using advanced data mining algorithm (case study: Kalat city). Quantitative Geomorphology Research, 10(4), 116-137. doi: 10.22034/gmpj.2022.291242.1284 [In Persian]

Gomez, H., & Kavzoglu, T. (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78(1–2), 11–27. doi: 10.1016/j.enggeo.2004.10.004

Huang, F., Xiong, H., Yao, C., Catani, F., Zhou, C., & Huang, J. (2023b). Uncertainties of landslide susceptibility prediction considering different landslide types. Journal of Rock Mechanics and Geotechnical Engineering, 15(11), 2954-2972.  doi: 10.1016/j.jrmge.2023.03.001.

Huang, W., Ding, M., Li, ZH., Yu, J., Ge, D., Liu, Q., & Yang, J. (2023a). Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms. Catena, 222, 106866. doi:10.1016/j.catena.2022.106866

Kavzoglu, T, Sahin, E.K., & Colkesen, I. (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425-439. doi:10.1007/s10346-013-0391-7

Kia, E., & Karimi, V.A. (2021). Investigation of temperature and rainfall parameters of Haraz River Basin affected by climate change. Journal of Natural Environment Hazards, 9(26), 145-160. doi:10.22111/jneh.2020.32606.1596. [In Persian]

Kornejady, A., & Pourghasemi, H.R. (2019). Landslide susceptibility assessment using data mining models. Watershed Engineering and Management, 11(1), 28-43. doi:10.22092/ijwmse.2019.118436 [In Persian]

Lan, H.X., Zhou, C.H., Wang, L.J. Zhang, H.J., & Li, R.H. (2004). Landslide watershed, Yunnan, China. Engineering Geology, 76, 101-128. doi: 10.1016/j.enggeo.2004.06.009

Lee, S, Ryu, J.H, Lee, M.J., & Won, J.S. (2006). The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Mathematical Geology, 38(2), 199-220. doi:10.1007/s11004-005-9012-x

Lee, S., Ryu J.H., Lee, M.J. & Won, J.S. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 44, 820–833. doi:10.1007/s00254-003-0825-y

Lima, P., Steger, S., Glade, T., & Mergili, M. (2023). Conventional data-driven landslide susceptibility models may only tell us half of the story: Potential underestimation of landslide impact areas depending on the modeling design, Geomorphology, 430, 108638. doi:10.1016/j.geomorph.2023.108638.

Melesse, A.M., Khosravi, K., Tiefenbacher, J.P., Heddam, S., Kim, S., Mosavi, A., & Pham, B.T. (2020). River water salinity prediction using Hybrid machine learning models. Water, 12(10), 2951. doi:10.3390/w12102951

Moghim, H., & Najabat, M. (2019). Comparison of the efficiency of modified Nielsen models and their relative effect in landslide risk zoning in Sedparsian watershed, Fars province. Watershed Engineering and Management, 11(1), 272-264. doi:10.22092/ijwmse.2019.118748 [In Persian]

Montaseri, M., & Zaman Zad Ghavidel, S. (2017). Comparing the performance of artificial intelligence models in estimating water quality parameters in periods of low and high water flow. Journal of Water and Soil, 30(6), 1733-1747. [In Persian]

Ngo, Ph.T.T., Panahi, M., Khosravi, KH., Ghorbanzadeh, O., Kariminejad, N., Artemi Cerda, A., & Lee, S. (2021). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2), 505-519. doi:10.1016/j.gsf.2020.06.013

Nojavan, M.R., Shahzaidi, S.S., Davoudi, M., & Amin al-Raaiai, H. (2019). Landslide risk zoning using the combination of two hierarchical and fuzzy analysis process models. Quantitative Geomorphology Researches, 7(4), 159-142. dor:20.1001.1.22519424.1398.7.4.9.3 [In Persian]

Norouzi Goshbalag, H., & Nadiri, A. (2018). Forecasting the underground water level of Bukan Plain using fuzzy logic, random forest and neural network models. Pasture and Watershed Quarterly, 72(1), 291-306. doi:10.22059/jrwm.2018.68924 [In Persian]

Pal, M., & Deswal, S. (2010). Modelling plie capacity using Gaussian process regression. Computers and geotechnics, 37(7-8), 942-947. doi:10.1016/j.compgeo.2010.07.012

Pourghasemi, H.R, Yansari, Z.T., Panagos, P., & Pradhan, B. (2018). Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arabian Journal of Geosciences, 11(9), 193. doi:10.1007/s12517-018-3531-5

Pourghasemi, H.R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision?. Catena, 162, 177-192. doi:10.1016/j.catena.2017.
11.022

Rodriguez, V., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sánchez, J.P. (2012). An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogram Remote Sens, 67, 9-104. doi:10.1016/j.isprsjprs.2011.11.002

Rossi, M., Guzzetti, F., Salvati, P., Donnini, M., Napolitano, E., & Bianch, C. (2019). A predictive model of societal landslide risk in Italy. Earth-Science Reviews, 196, 1-19. doi:10.1016/j.earscirev.2019.04.021

Sepahvand, A., Sihag, P., Singh, B., & Zand, M. (2018). Comparative evaluation of infiltration models. KSCE Journal of Civil Engineering, 22, 4173–4184. doi:10.1007/s12205-018-1347-1

Sepahvand, A., Singh B.,Ghobadi, M., & Sihag, P. (2021). Estimation of infiltration rate using data-driven models. Arabian Journal of Geosciences, 14(42), doi:10.1007/s12517-020-06245-2.

Sepahvand, A.R.,  Ahmadi, H., Nazari Samani, A.A., Fiznia, S. (2018). Landforms classification by Topographic Position Index and assessment of teh relation between landforms and lithological features. Researches in Earth Sciences, 9(1-Serial Number 33), 30-4. doi: 10.29252/esrj.9.1.30 [In Persian]

Sepahvand, A.R., Moradi, H.R., & Abdul Maleki, P. (2016). Landslide risk zoning using artificial neural network in a part of Haraz watershed. Watershed Management Research, 29(4), 9-19. doi:10.22092/wmej.2017.115313 [In Persian]

Silalahi, F.E.S., Pamela., Arifanti, Y., & Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Official Journal of the Asia Oceania Geosciences Society (AOGS), 6(10), 1-17. doi:10.1186/s40562-019-0140-4

Singh, P., Singh, V.P., Angelaki, A., Kumar, V., Sepahvand, A., & Golia, E. (2019). Modelling of infiltration using artificial intelligence techniques in semi-arid Iran. Hydrological Sciences Journal, 64(13), 1647-1658. doi:10.1080/02626667.2019.1659965

Solaimani, K., Zandi, J., & Habibnejad, M. (2015). Evaluating the efficiency of frequency ratio, bivariate (Wi), and (Wf) methods of landslide susceptibility mapping. Journal of Geoscience, 24(94), 41-50.

Tran, T.H., Dam, D.D., Jalal, F.E., Al-Ansari, N., Ho, L.S., Phong, T.V., Iqbal, M., Le, H.V., Nguyen, H.B.T., Prakash, I., & Pham, B.T. (2021). GIS-based soft computing models for landslide susceptibility mapping, hindawi. Mathematical Problems in Engineering, 9914650. doi:10.1155/2021/9914650

Trigila, A., Iadanza, C., Esposito, C., & Scarascia-Mugnozza, G. (2015). Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology, 249, 119–136. doi:10.1016/j.
geomorph.2015.06.001

Vapnik, V. (1999), An overview of statistical learning theory. IEEE Transactions on Neural Network, 10(5), 988-999. http://www.mit.edu/~6.454/www_spring_2001/emin/slt.pdf

Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers and Geosciences, 35(6), 1125–1138. doi:10.1016/j.cageo.2008.08.007