Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

References

• Angeli M-G, Pasuto A, Silvano S. 2000. A critical review of landslide monitoring experiences. Eng Geol. 55(3):133–147.
   Web of Science ®Google Scholar
• Azarafza M, Ghazifard A, Akgün H, Asghari-Kaljahi E. 2018. Landslide susceptibility assessment of South Pars Special Zone, southwest Iran. Environ Earth Sci. 77:1–29.
   Web of Science ®Google Scholar
• Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R. 2021. Deep learning-based landslide susceptibility mapping. Sci Reports. 11(1):24112.
   PubMed Web of Science ®Google Scholar
• Bao L, Zhang G, Hu X, Wu S, Liu X. 2021. Stage division of landslide deformation and prediction of critical sliding based on inverse logistic function. Energies. 14(4)
   Web of Science ®Google Scholar
• Bouali EH, Oommen T, Escobar-Wolf R. 2019. Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: a case study in the Portuguese Bend Landslide Complex, California. Remote Sens. 11(8).
   Google Scholar
• Cai J, Liu G, Jia H, Zhang B, Wu R, Fu Y, Xiang W, Mao W, Wang X, Zhang R. 2022. A new algorithm for landslide dynamic monitoring with high temporal resolution by Kalman filter integration of multiplatform time-series InSAR processing. Int J Appl Earth Observ Geoinf. 110.
   Web of Science ®Google Scholar
• Cao C, Zhang W, Chen J, Shan B, Song S, Zhan J. 2021. Quantitative estimation of debris flow source materials by integrating multi-source data: a case study. Eng Geol. 291:106222.
   Web of Science ®Google Scholar
• Cao C, Zhu K, Song T, Bai J, Zhang W, Chen J, Song S. 2022. Comparative study on potential landslide identification with ALOS-2 and Sentinel-1A data in heavy forest reach, Upstream of the Jinsha River. Remote Sens. 14(9):1962.
   Google Scholar
• Chung C-C, Lin C-P, Ngui YJ, Lin W-C, Yang C-S. 2022. Improved technical guide from physical model tests for TDR landslide monitoring. Eng Geol. 296.
   Web of Science ®Google Scholar
• Cui S, Wu H, Pei X, Yang Q, Huang R, Guo B. 2022. Characterizing the spatial distribution, frequency, geomorphological and geological controls on landslides triggered by the 1933 Mw 7.3 Diexi Earthquake, Sichuan. China Geomorphol. 403:108177.
   Web of Science ®Google Scholar
• Dong J, Zhang L, Li M, Yu Y, Liao M, Gong J, Luo H. 2018. Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets. Landslides. 15(1):135–144.
   Web of Science ®Google Scholar
• Du J, Yin K, Lacasse S. 2013. Displacement prediction in colluvial landslides, three Gorges reservoir, China. Landslides. 10(2):203–218.
   Web of Science ®Google Scholar
• Eker R, Aydın A. 2021. Long-term retrospective investigation of a large, deep-seated, and slow-moving landslide using InSAR time series, historical aerial photographs, and UAV data: the case of Devrek landslide (NW Turkey). Catena. 196:104895.
   Web of Science ®Google Scholar
• Fan X, Xu Q, Scaringi G, Dai L, Li W, Dong X, Zhu X, Pei X, Dai K, Havenith H-B. 2017. Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides. 14(6):2129–2146.
   Web of Science ®Google Scholar
• Fobert M-A, Singhroy V, Spray JG. 2021. InSAR monitoring of landslide activity in Dominica. Remote Sens. 13(4):815.
   Google Scholar
• Frattini P, Crosta GB, Rossini M, Allievi J. 2018. Activity and kinematic behaviour of deep-seated landslides from PS-InSAR displacement rate measurements. Landslides. 15(6):1053–1070.
   Web of Science ®Google Scholar
• Gao Y, Chen X, Tu R, Chen G, Luo T, Xue D. 2022. Prediction of landslide displacement based on the combined VMD-stacked LSTM-TAR model. Remote Sens. 14(5).
   Google Scholar
• Gao H, He L, He Z-w, Bai W-q 2022. Early landslide mapping with slope units division and multi-scale object-based image analysis—A case study in the Xianshui river basin of Sichuan, China. J Mt Sci. 19(6):1618–1632.
   Web of Science ®Google Scholar
• Huang F, Yin K, Zhang G, Gui L, Yang B, Liu L. 2016. Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory. Environ Earth Sci. 75(20):1–18.
   Web of Science ®Google Scholar
• Jibson RW. 2007. Regression models for estimating coseismic landslide displacement. Eng Geol. 91(2–4):209–218.
   Web of Science ®Google Scholar
• Kalantar B, Ueda N, Saeidi V, Ahmadi K, Halin AA, Shabani F. 2020. Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens. 12(11):1737.
   Google Scholar
• Lauknes T, Dehls J, Larsen Y, Henderson I, Zebker H. 2007. Regional landslide mapping and monitoring in norway using SBAS InSAR. In: AGU Fall Meeting Abstracts, G53A-08.
   Google Scholar
• Li M, Zhang L, Ding C, Li W, Luo H, Liao M, Xu Q. 2020. Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event. Remote Sens Environ. 240.
   Web of Science ®Google Scholar
• Li Y, Chen J, Zhou F, Song S, Zhang Y, Gu F, Cao C. 2020. Identification of ancient river-blocking events and analysis of the mechanisms for the formation of landslide dams in the Suwalong section of the upper Jinsha River, SE Tibetan Plateau. Geomorphology. 368:107351.
   Web of Science ®Google Scholar
• Lian C, Zeng Z, Yao W, Tang H. 2015. Multiple neural networks switched prediction for landslide displacement. Eng Geol. 186:91–99.
   Web of Science ®Google Scholar
• Lian C, Zeng Z, Yao W, Tang H, Chen CLP. 2016. Landslide displacement prediction with uncertainty based on neural networks with random hidden weights. IEEE Trans Neural Netw Learn Syst. 27(12):2683–2695.
   PubMed Web of Science ®Google Scholar
• Liao K, Wu Y, Miao F, Li L, Xue Y. 2021. Time-varying reliability analysis of Majiagou landslide based on weakening of hydro-fluctuation belt under wetting-drying cycles. Landslides. 18(1):267–280.
   Web of Science ®Google Scholar
• Liu Z, Shao J, Xu W, Chen H, Shi C. 2014. Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides. 11(5):889–896.
   Web of Science ®Google Scholar
• Long J, Li C, Liu Y, Feng P, Zuo Q. 2022. A multi-feature fusion transfer learning method for displacement prediction of rainfall reservoir-induced landslide with step-like deformation characteristics. Eng Geol. 297.
   Web of Science ®Google Scholar
• Lucieer A, Jong S, Turner D. 2014. Mapping landslide displacements using structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr. 38(1):97–116.
   Web of Science ®Google Scholar
• Mantovani F, Soeters R, Van Westen C. 1996. Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology. 15(3–4):213–225.
   Web of Science ®Google Scholar
• McCann D, Forster A. 1990. Reconnaissance geophysical methods in landslide investigations. Eng Geol. 29(1):59–78.
   Web of Science ®Google Scholar
• Mengistu F, Suryabhagavan K, Raghuvanshi TK, Lewi E. 2019. Landslide Hazard zonation and slope instability assessment using optical and InSAR data: a case study from Gidole town and its surrounding areas, southern Ethiopia. Remote Sens Land. 3(1):1–14.
   Google Scholar
• Mondini AC, Guzzetti F, Chang K-T, Monserrat O, Martha TR, Manconi A. 2021. Landslide failures detection and mapping using Synthetic Aperture Radar: past, present and future. Earth-Sci Rev. 216.
   Web of Science ®Google Scholar
• Nanehkaran YA, Mao Y, Azarafza M, Kockar MK, Zhu H-H. 2021. Fuzzy-based multiple decision method for landslide susceptibility and hazard assessment: a case study of Tabriz, Iran. Geomech Eng. 24(5):407–418.
   Web of Science ®Google Scholar
• Nikoobakht S, Azarafza M, Akgün H, Derakhshani R. 2022. Landslide susceptibility assessment by using convolutional neural network. Appl Sci. 12(12):5992.
   Google Scholar
• Nourani V, Alami MT, Aminfar MH. 2009. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell. 22(3):466–472.
   Web of Science ®Google Scholar
• Pecoraro G, Calvello M, Piciullo L. 2018. Monitoring strategies for local landslide early warning systems. Landslides. 16(2):213–231.
   Web of Science ®Google Scholar
• Rib HT, Liang T. 1978. Recognition and identification. Transportation Research Board Special Report. 176.
   Google Scholar
• Saba SB, van der Meijde M, van der Werff H. 2010. Spatiotemporal landslide detection for the 2005 Kashmir earthquake region. Geomorphology. 124(1–2):17–25.
   Web of Science ®Google Scholar
• Sauchyn DJ, Trench N. 1978. Landsat applied to landslide mapping. Photogramm Eng Remote Sens. 44(6):735–741.
   Web of Science ®Google Scholar
• Scaioni M. 2013. Remote sensing for landslide investigations: from research into practice. In: MDPI, p. 5488–5492.
   Google Scholar
• Sezer E. 2010. A computer program for fractal dimension (FRACEK) with application on type of mass movement characterization. Comput Geosci. 36(3):391–396.
   Web of Science ®Google Scholar
• Shankar H, Singh D, Chauhan P. 2022. Landslide deformation and temporal prediction of slope failure in Himalayan terrain using PSInSAR and Sentinel-1 data. Adv Space Res.
   Web of Science ®Google Scholar
• Turner D, Lucieer A, De Jong SM. 2015. Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sens. 7(2):1736–1757.
   Web of Science ®Google Scholar
• Wang H, Liu S, Xu W, Yan L, Qu X, Xie W-C. 2020. Numerical investigation on the sliding process and deposit feature of an earthquake-induced landslide: a case study. Landslides. 17(11):2671–2682.
   Web of Science ®Google Scholar
• Wang Y, Tang H, Huang J, Wen T, Ma J, Zhang J. 2022. A comparative study of different machine learning methods for reservoir landslide displacement prediction. Eng Geol. 298
   Web of Science ®Google Scholar
• Wasowski J, Bovenga F. 2014. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: current issues and future perspectives. Eng Geol. 174:103–138.
   Web of Science ®Google Scholar
• Xie P, Zhou A, Chai B. 2019. The application of long short-term memory (LSTM) method on displacement prediction of multifactor-induced landslides. IEEE Access. 7:54305–54311.
   Web of Science ®Google Scholar
• Zhang Y, Tang J, Cheng Y, Huang L, Guo F, Yin X, Li N. 2022. Prediction of landslide displacement with dynamic features using intelligent approaches. Int J Mining Sci Technol. 32(3):539–549.
   Web of Science ®Google Scholar
• Zhang Y, Wang X, Tang H. 2019. An improved Elman neural network with piecewise weighted gradient for time series prediction. Neurocomputing. 359:199–208.
   Web of Science ®Google Scholar
• Zhang X, Zhu C, He M, Dong M, Zhang G, Zhang F. 2021. Failure mechanism and long short-term memory neural network model for landslide risk prediction. Remote Sens. 14(1):166.
   Google Scholar
• Zhao C, Lu Z, Zhang Q, de La Fuente J. 2012. Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA. Remote Sens Environ. 124:348–359.
   Web of Science ®Google Scholar
• Zhuang Y, Xing A, Leng Y, Bilal M, Zhang Y, Jin K, He J. 2021. Investigation of characteristics of long Runout landslides based on the multi-source data collaboration: a case study of the Shuicheng Basalt Landslide in Guizhou, China. Rock Mech Rock Eng. 54(8):3783–3798.
   Web of Science ®Google Scholar