{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T03:09:06Z","timestamp":1781147346291,"version":"3.54.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["50808149"],"award-info":[{"award-number":["50808149"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01600-3","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:45:37Z","timestamp":1733957137000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["High-accuracy slope stability analysis using data-driven and attention-based deep learning model"],"prefix":"10.1007","volume":"18","author":[{"given":"Yangli","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiying","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhe","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"issue":"9","key":"1600_CR1","doi-asserted-by":"publisher","first-page":"5463","DOI":"10.1007\/s12665-014-3800-x","volume":"73","author":"JA Abdalla","year":"2015","unstructured":"Abdalla JA, Attom MF, Hawileh R (2015) Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environmental Earth Sciences 73(9):5463\u20135477. https:\/\/doi.org\/10.1007\/s12665-014-3800-x","journal-title":"Environmental Earth Sciences"},{"key":"1600_CR2","doi-asserted-by":"publisher","first-page":"109033","DOI":"10.1016\/j.geomorph.2023.109033","volume":"448","author":"A Achu","year":"2023","unstructured":"Achu A, Thomas J, Aju C, Vijith H, Gopinath G (2023) Redefining landslide susceptibility under extreme rainfall events using deep learning. Geomorphology 448:109033","journal-title":"Geomorphology"},{"issue":"3\u20134","key":"1600_CR3","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1108\/02644401111118132","volume":"28","author":"AH Alavi","year":"2011","unstructured":"Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3\u20134):242\u2013274. https:\/\/doi.org\/10.1108\/02644401111118132","journal-title":"Eng Comput"},{"issue":"12","key":"1600_CR4","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1080\/17538947.2021.1988163","volume":"14","author":"M Azarafza","year":"2021","unstructured":"Azarafza M, Akgun H, Ghazifard A, Asghari-Kaljahi E, Rahnamarad J, Derakhshani R (2021) Discontinuous rock slope stability analysis by limit equilibrium approaches - a review. Int J Digital Earth 14(12):1918\u20131941. https:\/\/doi.org\/10.1080\/17538947.2021.1988163","journal-title":"Int J Digital Earth"},{"key":"1600_CR5","doi-asserted-by":"publisher","first-page":"150623","DOI":"10.1109\/access.2021.3123501","volume":"9","author":"B Azmoon","year":"2021","unstructured":"Azmoon B, Biniyaz A, Liu Z, Sun Y (2021) Image-data-driven slope stability analysis for preventing landslides using deep learning. Ieee Access 9:150623\u2013150636. https:\/\/doi.org\/10.1109\/access.2021.3123501","journal-title":"Ieee Access"},{"issue":"1","key":"1600_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJGEE.298988","volume":"13","author":"A Bardhan","year":"2022","unstructured":"Bardhan A, Samui P (2022) Application of artificial intelligence techniques in slope stability analysis: a short review and future prospects. Int J Geotech Earthquake Eng (IJGEE) 13(1):1\u201322","journal-title":"Int J Geotech Earthquake Eng (IJGEE)"},{"issue":"21","key":"1600_CR7","doi-asserted-by":"publisher","first-page":"8503","DOI":"10.3390\/s22218503","volume":"22","author":"A Biniyaz","year":"2022","unstructured":"Biniyaz A, Azmoon B, Liu Z (2022) Intelligent control of groundwater in slopes with deep reinforcement learning. Sensors 22(21):8503. https:\/\/doi.org\/10.3390\/s22218503","journal-title":"Sensors"},{"issue":"14","key":"1600_CR8","doi-asserted-by":"publisher","first-page":"104426","DOI":"10.1016\/j.catena.2019.104426","volume":"188","author":"DT Bui","year":"2020","unstructured":"Bui DT, Tsangaratos P, Nguyen VT, Liem NV, Trinh PT (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena 188(14):104426. https:\/\/doi.org\/10.1016\/j.catena.2019.104426","journal-title":"Catena"},{"issue":"2","key":"1600_CR9","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","volume":"13","author":"DT Bui","year":"2016","unstructured":"Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361\u2013378. https:\/\/doi.org\/10.1007\/s10346-015-0557-6","journal-title":"Landslides"},{"key":"1600_CR10","unstructured":"Cala M, Flisiak J (2001) Slope stability analysis with FLAC and limit equilibrium methods. [Flac and numerical modeling in geomechanics]. 2nd International FLAC Symposium, Lyon, France"},{"key":"1600_CR11","doi-asserted-by":"publisher","unstructured":"Chen GF, Kang XY, Lin MS, Teng S, Liu ZC (2023) Stability prediction of soil slopes based on digital twinning and deep learning. Appl Sci-Basel 13(11):24. Article 6470. https:\/\/doi.org\/10.3390\/app13116470","DOI":"10.3390\/app13116470"},{"issue":"1","key":"1600_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1680\/geot.1983.33.1.31","volume":"33","author":"RH Chen","year":"1983","unstructured":"Chen RH, Chameau J-L (1983) Three-dimensional limit equilibrium analysis of slopes. Geotechnique 33(1):31\u201340. https:\/\/doi.org\/10.1680\/geot.1983.33.1.31","journal-title":"Geotechnique"},{"issue":"6","key":"1600_CR13","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1109\/jstars.2014.2329330","volume":"7","author":"YS Chen","year":"2014","unstructured":"Chen YS, Lin ZH, Zhao X, Wang G, Gu YF (2014) Deep learning-based classification of hyperspectral data. Ieee J Sel Topics Appl Earth Obs Remote Sens 7(6):2094\u20132107. https:\/\/doi.org\/10.1109\/jstars.2014.2329330","journal-title":"Ieee J Sel Topics Appl Earth Obs Remote Sens"},{"issue":"1","key":"1600_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.16285\/j.rsm.2017.2577","volume":"40","author":"ZH Chen","year":"2019","unstructured":"Chen ZH, Guo N (2019) New developments of mechanics and application for unsaturated soils and special soils. Rock Soil Mech 40(1):1\u201354. https:\/\/doi.org\/10.16285\/j.rsm.2017.2577","journal-title":"Rock Soil Mech"},{"issue":"3","key":"1600_CR15","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.compgeo.2006.10.011","volume":"34","author":"YM Cheng","year":"2007","unstructured":"Cheng YM, Lansivaara T, Wei WB (2007) Two-dimensional slope stability analysis by limit equilibrium and strength reduction methods. Comput Geotech 34(3):137\u2013150. https:\/\/doi.org\/10.1016\/j.compgeo.2006.10.011","journal-title":"Comput Geotech"},{"issue":"12","key":"1600_CR16","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.1061\/(ASCE)0733-9410(1994)120:12(2180)","volume":"120","author":"JT Christian","year":"1994","unstructured":"Christian JT, Ladd CC, Baecher GB (1994) Reliability applied to slope stability analysis. J Geotech Eng 120(12):2180\u20132207. https:\/\/doi.org\/10.1061\/(ASCE)0733-9410(1994)120:12(2180)","journal-title":"J Geotech Eng"},{"issue":"1","key":"1600_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/msp.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks an overview. IEEE Signal Process Mag 35(1):53\u201365. https:\/\/doi.org\/10.1109\/msp.2017.2765202","journal-title":"IEEE Signal Process Mag"},{"issue":"6","key":"1600_CR18","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1007\/s12583-020-1331-9","volume":"31","author":"RE Criss","year":"2020","unstructured":"Criss RE, Yao WM, Li CD, Tang HM (2020) A predictive, two-parameter model for the movement of reservoir landslides. J Earth Sci 31(6):1051\u20131057. https:\/\/doi.org\/10.1007\/s12583-020-1331-9","journal-title":"J Earth Sci"},{"issue":"3\u20134","key":"1600_CR19","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/s0169-555x(01)00087-3","volume":"42","author":"FC Dai","year":"2002","unstructured":"Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island Hong Kong. Geomorphology 42(3\u20134):213\u2013228. https:\/\/doi.org\/10.1016\/s0169-555x(01)00087-3","journal-title":"Geomorphology"},{"key":"1600_CR20","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2015.12.114","volume":"192","author":"A de Myttenaere","year":"2016","unstructured":"de Myttenaere A, Golden B, Le Grand B, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2015.12.114","journal-title":"Neurocomputing"},{"issue":"5","key":"1600_CR21","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1109\/tpami.2019.2956703","volume":"43","author":"X Dong","year":"2021","unstructured":"Dong X, Shen J, Wang W, Shao L, Ling H, Porikli F (2021) Dynamical hyperparameter optimization via deep reinforcement learning in tracking. IEEE Trans Pattern Anal Mach Intell 43(5):1515\u20131529. https:\/\/doi.org\/10.1109\/tpami.2019.2956703","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1\u20132","key":"1600_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/0013-7952(80)90003-4","volume":"16","author":"JM Duncan","year":"1980","unstructured":"Duncan JM, Wright SG (1980) The accuracy of equilibrium methods of slope stability analysis. Eng Geol 16(1\u20132):5\u201317","journal-title":"Eng Geol"},{"key":"1600_CR23","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.cageo.2012.09.003","volume":"51","author":"Y Erzin","year":"2013","unstructured":"Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51:305\u2013313. https:\/\/doi.org\/10.1016\/j.cageo.2012.09.003","journal-title":"Comput Geosci"},{"key":"1600_CR24","doi-asserted-by":"publisher","first-page":"106836","DOI":"10.1016\/j.enggeo.2022.106836","volume":"309","author":"I Farmakis","year":"2022","unstructured":"Farmakis I, DiFrancesco P-M, Hutchinson DJ, Vlachopoulos N (2022) Rockfall detection using LiDAR and deep learning. Eng Geol 309:106836","journal-title":"Eng Geol"},{"issue":"14","key":"1600_CR25","doi-asserted-by":"publisher","first-page":"105968","DOI":"10.1016\/j.enggeo.2020.105968","volume":"281","author":"BS Firincioglu","year":"2021","unstructured":"Firincioglu BS, Ercanoglu M (2021) Insights and perspectives into the limit equilibrium method from 2D and 3D analyses. Eng Geol 281(14):105968. https:\/\/doi.org\/10.1016\/j.enggeo.2020.105968","journal-title":"Eng Geol"},{"issue":"2","key":"1600_CR26","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137\u2013144. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2014.10.007","journal-title":"Int J Inf Manage"},{"key":"1600_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cageo.2015.04.007","volume":"81","author":"JN Goetz","year":"2015","unstructured":"Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1\u201311. https:\/\/doi.org\/10.1016\/j.cageo.2015.04.007","journal-title":"Comput Geosci"},{"key":"1600_CR28","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press"},{"issue":"3","key":"1600_CR29","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1680\/geot.1999.49.3.387","volume":"49","author":"DV Griffiths","year":"1999","unstructured":"Griffiths DV, Lane PA (1999) Slope stability analysis by finite elements. Geotechnique 49(3):387\u2013403. https:\/\/doi.org\/10.1680\/geot.1999.49.3.387","journal-title":"Geotechnique"},{"key":"1600_CR30","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ et al (2020) Array programming with NumPy. Nature 585:357\u2013362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"1600_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2_4","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S (1997) Long short-term memory. Neural Computation MIT-Press. https:\/\/doi.org\/10.1007\/978-3-642-24797-2_4","journal-title":"Neural Computation MIT-Press"},{"issue":"2","key":"1600_CR32","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1061\/(asce)1090-0241(2006)132:2(183)","volume":"132","author":"SC Hsu","year":"2006","unstructured":"Hsu SC, Nelson PP (2006) Material spatial variability and slope stability for weak rock masses [Article]. J Geotech Geoenviron Eng 132(2):183\u2013193. https:\/\/doi.org\/10.1061\/(asce)1090-0241(2006)132:2(183)","journal-title":"J Geotech Geoenviron Eng"},{"key":"1600_CR33","doi-asserted-by":"publisher","unstructured":"Huang FM, Xiong HW, Chen SX, Lv ZT, Huang JS, Chang Z L, Catani F (2023) Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models. Int J Coal Sci Technol 10(1):14, 18. https:\/\/doi.org\/10.1007\/s40789-023-00579-4","DOI":"10.1007\/s40789-023-00579-4"},{"issue":"2","key":"1600_CR34","doi-asserted-by":"publisher","first-page":"617","DOI":"10.17559\/tv-20150314105216","volume":"23","author":"FM Huang","year":"2016","unstructured":"Huang FM, Yin KL, He T, Zhou C, Zhang J (2016) Influencing factor analysis and displacement prediction in reservoir landslides - a case study of three gorges reservoir (China). Tehnicki Vjesnik- Gazette 23(2):617\u2013626. https:\/\/doi.org\/10.17559\/tv-20150314105216","journal-title":"Tehnicki Vjesnik- Gazette"},{"key":"1600_CR35","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compgeo.2014.08.010","volume":"63","author":"F Kang","year":"2015","unstructured":"Kang F, Han SX, Salgado R, Li JJ (2015) System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling. Comput Geotech 63:13\u201325. https:\/\/doi.org\/10.1016\/j.compgeo.2014.08.010","journal-title":"Comput Geotech"},{"issue":"13","key":"1600_CR36","doi-asserted-by":"publisher","first-page":"100745","DOI":"10.1016\/j.trgeo.2022.100745","volume":"34","author":"D Karir","year":"2022","unstructured":"Karir D, Ray A, Bharati AK, Chaturvedi U, Rai R, Khandelwal M (2022) Stability prediction of a natural and man-made slope using various machine learning algorithms. Trans Geotech 34(13):100745. https:\/\/doi.org\/10.1016\/j.trgeo.2022.100745","journal-title":"Trans Geotech"},{"issue":"7","key":"1600_CR37","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1061\/(asce)1090-0241(2002)128:7(546)","volume":"128","author":"J Kim","year":"2002","unstructured":"Kim J, Salgado R, Lee J (2002) Stability analysis of complex soil slopes using limit analysis. J Geotech Geoenviron Eng 128(7):546\u2013557. https:\/\/doi.org\/10.1061\/(asce)1090-0241(2002)128:7(546)","journal-title":"J Geotech Geoenviron Eng"},{"issue":"1","key":"1600_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/s0266-352x(96)00010-9","volume":"20","author":"JS Kim","year":"1997","unstructured":"Kim JS, Kim JY, Lee SR (1997) Analysis of soil nailed earth slope by discrete element method. Comput Geotech 20(1):1\u201314. https:\/\/doi.org\/10.1016\/s0266-352x(96)00010-9","journal-title":"Comput Geotech"},{"key":"1600_CR39","doi-asserted-by":"crossref","unstructured":"Koner R, Chakravarty D (2016) Numerical analysis of rainfall effects in external overburden dump. Int J Mining Sci Technol 26(5):825\u2013831. Article 2095\u20132686(2016)26:5<825:Naorei>2.0.Tx;2-p. <Go to ISI>:\/\/CSCD:5846074","DOI":"10.1016\/j.ijmst.2016.05.048"},{"issue":"2","key":"1600_CR40","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1680\/jgele.18.00022","volume":"8","author":"S Kumar","year":"2018","unstructured":"Kumar S, Basudhar PK (2018) A neural network model for slope stability computations. Geotechnique Letters 8(2):149\u2013154. https:\/\/doi.org\/10.1680\/jgele.18.00022","journal-title":"Geotechnique Letters"},{"issue":"1","key":"1600_CR41","doi-asserted-by":"publisher","first-page":"77","DOI":"10.2307\/2346413","volume":"29","author":"P Lerman","year":"1980","unstructured":"Lerman P (1980) Fitting segmented regression models by grid search. J R Stat Soc: Ser c: Appl Stat 29(1):77\u201384. https:\/\/doi.org\/10.2307\/2346413","journal-title":"J R Stat Soc: Ser c: Appl Stat"},{"key":"1600_CR42","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.cageo.2017.03.007","volume":"103","author":"N Li","year":"2017","unstructured":"Li N, Hao HZ, Gu Q, Wang DR, Hu XM (2017) A transfer learning method for automatic identification of sandstone microscopic images. Comput Geosci 103:111\u2013121. https:\/\/doi.org\/10.1016\/j.cageo.2017.03.007","journal-title":"Comput Geosci"},{"key":"1600_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compgeo.2015.07.011","volume":"70","author":"K Lim","year":"2015","unstructured":"Lim K, Li AJ, Lyamin AV (2015) Three-dimensional slope stability assessment of two-layered undrained clay. Comput Geotech 70:1\u201317. https:\/\/doi.org\/10.1016\/j.compgeo.2015.07.011","journal-title":"Comput Geotech"},{"key":"1600_CR44","doi-asserted-by":"publisher","unstructured":"Lin M, Chen X, Chen G, Zhao Z, Bassir D (2024) Stability prediction of multi-material complex slopes based on self-attention convolutional neural networks. Stochastic Environ Res Risk Assessment 1\u201317. https:\/\/doi.org\/10.1007\/s00477-024-02792-2","DOI":"10.1007\/s00477-024-02792-2"},{"key":"1600_CR45","unstructured":"Liu M, Zhu R (2002) Case-based reasoning approach to slope stability evaluation based on fuzzy analogy preferred ratio. Chin J Rock Mech Eng 21(8):1188\u20131193. Article 1000\u20136915(2002)21:8<1188:Jymhxs>2.0.Tx;2-c. <Go to ISI>:\/\/CSCD:1018224"},{"issue":"33","key":"1600_CR46","doi-asserted-by":"publisher","first-page":"103858","DOI":"10.1016\/j.earscirev.2021.103858","volume":"223","author":"ZJ Ma","year":"2021","unstructured":"Ma ZJ, Mei G (2021) Deep learning for geological hazards analysis: data, models, applications, and opportunities. Earth-Sci Rev 223(33):103858. https:\/\/doi.org\/10.1016\/j.earscirev.2021.103858","journal-title":"Earth-Sci Rev"},{"issue":"2","key":"1600_CR47","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1007\/s11069-021-05115-8","volume":"111","author":"A Mahmoodzadeh","year":"2022","unstructured":"Mahmoodzadeh A, Mohammadi M, Ali HFH, Ibrahim HH, Abdulhamid SN, Nejati HR (2022) Prediction of safety factors for slope stability: comparison of machine learning techniques. Nat Hazards 111(2):1771\u20131799. https:\/\/doi.org\/10.1007\/s11069-021-05115-8","journal-title":"Nat Hazards"},{"issue":"2","key":"1600_CR48","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1680\/geot.1995.45.2.283","volume":"45","author":"RL Michalowski","year":"1995","unstructured":"Michalowski RL (1995) Slope stability analysis: A kinematical approach. Geotechnique 45(2):283\u2013293. https:\/\/doi.org\/10.1680\/geot.1995.45.2.283","journal-title":"Geotechnique"},{"issue":"4","key":"1600_CR49","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1061\/(asce)gt.1943-5606.0000251","volume":"136","author":"RL Michalowski","year":"2010","unstructured":"Michalowski RL (2010) Limit Analysis and Stability Charts for 3D Slope Failures. J Geotech Geoenviron Eng 136(4):583\u2013593. https:\/\/doi.org\/10.1061\/(asce)gt.1943-5606.0000251","journal-title":"J Geotech Geoenviron Eng"},{"key":"1600_CR50","unstructured":"Mitchell JK, Soga K (2005) Fundamentals of soil behavior (Vol. 3). John Wiley & Sons New York."},{"issue":"7","key":"1600_CR51","doi-asserted-by":"publisher","first-page":"e3998","DOI":"10.1002\/ett.3998","volume":"32","author":"A Mohan","year":"2021","unstructured":"Mohan A, Singh AK, Kumar B, Dwivedi R (2021) Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerging Telecommun Technol 32(7):e3998","journal-title":"Trans Emerging Telecommun Technol"},{"issue":"1","key":"1600_CR52","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1680\/geot.1965.15.1.79","volume":"15","author":"NR Morgenstern","year":"1965","unstructured":"Morgenstern NR, Price VE (1965) The Analysis of the stability of general slip Surfaces. Geotechnique 15(1):79\u201393. https:\/\/doi.org\/10.1680\/geot.1965.15.1.79","journal-title":"Geotechnique"},{"key":"1600_CR53","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"ZY Niu","year":"2021","unstructured":"Niu ZY, Zhong GQ, Yu H (2021) A review on the attention mechanism of deep learning [Review]. Neurocomputing 452:48\u201362. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.091","journal-title":"Neurocomputing"},{"key":"1600_CR54","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch. 31st Conference on neural information processing systems. Long Beach, CA, USA"},{"key":"1600_CR55","volume-title":"Foundation engineering","author":"RB Peck","year":"1991","unstructured":"Peck RB, Hanson WE, Thornburn TH (1991) Foundation engineering. John Wiley & Sons"},{"key":"1600_CR56","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.cie.2018.02.028","volume":"118","author":"C Qi","year":"2018","unstructured":"Qi C, Tang X (2018a) Slope stability prediction using integrated metaheuristic and machine learning approaches: a comparative study. Comput Ind Eng 118:112\u2013122. https:\/\/doi.org\/10.1016\/j.cie.2018.02.028","journal-title":"Comput Ind Eng"},{"issue":"15","key":"1600_CR57","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1002\/nag.2834","volume":"42","author":"CC Qi","year":"2018","unstructured":"Qi CC, Tang XL (2018b) A hybrid ensemble method for improved prediction of slope stability. Int J Numer Anal Meth Geomech 42(15):1823\u20131839. https:\/\/doi.org\/10.1002\/nag.2834","journal-title":"Int J Numer Anal Meth Geomech"},{"issue":"12","key":"1600_CR58","doi-asserted-by":"publisher","first-page":"100944","DOI":"10.1016\/j.aei.2019.100944","volume":"42","author":"KM Rashid","year":"2019","unstructured":"Rashid KM, Louis J (2019) Times-series data augmentation and deep learning for construction equipment activity recognition. Adv Eng Inf 42(12):100944. https:\/\/doi.org\/10.1016\/j.aei.2019.100944","journal-title":"Adv Eng Inf"},{"issue":"3","key":"1600_CR59","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.jrmge.2019.11.004","volume":"12","author":"HR Renani","year":"2020","unstructured":"Renani HR, Martin CD (2020) Factor of safety of strain-softening slopes. J Rock Mech Geotech Eng 12(3):473\u2013483. https:\/\/doi.org\/10.1016\/j.jrmge.2019.11.004","journal-title":"J Rock Mech Geotech Eng"},{"key":"1600_CR60","unstructured":"Rocscience Slide (2024) Slide compute. Rocscience Slide Online Help.\u00a0https:\/\/www.rocscience.com\/help\/slide2\/documentation\/slide-compute\/compute. Accessed 12 Jun 2024"},{"key":"1600_CR61","doi-asserted-by":"publisher","unstructured":"Sak H, Senior AW, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. https:\/\/doi.org\/10.21437\/Interspeech.2014-80","DOI":"10.21437\/Interspeech.2014-80"},{"issue":"7","key":"1600_CR62","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/s13201-019-1038-1","volume":"9","author":"F Salmasi","year":"2019","unstructured":"Salmasi F, Pradhan B, Nourani B (2019) Prediction of the sliding type and critical factor of safety in homogeneous finite slopes. Appl Water Sci 9(7):158. https:\/\/doi.org\/10.1007\/s13201-019-1038-1","journal-title":"Appl Water Sci"},{"key":"1600_CR63","doi-asserted-by":"publisher","unstructured":"Schmidt-Hieber J (2020) Nonparametric regression using deep neural networks with ReLU activation function. https:\/\/doi.org\/10.1214\/19-AOS1875","DOI":"10.1214\/19-AOS1875"},{"issue":"7","key":"1600_CR64","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1680\/geot.12.RL.001","volume":"63","author":"SW Sloan","year":"2013","unstructured":"Sloan SW (2013) Geotechnical stability analysis. Geotechnique 63(7):531\u2013572. https:\/\/doi.org\/10.1680\/geot.12.RL.001","journal-title":"Geotechnique"},{"key":"1600_CR65","doi-asserted-by":"publisher","first-page":"404","DOI":"10.4028\/www.scientific.net\/AMM.385-386.404","volume":"385\u2013386","author":"MH Su","year":"2013","unstructured":"Su MH, Ma XD, Li X (2013) Analysis of engineering geological mechanics on the jiasikou debris flow. Appl Mech Mater 385\u2013386:404\u2013407. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMM.385-386.404","journal-title":"Appl Mech Mater"},{"key":"1600_CR66","doi-asserted-by":"publisher","first-page":"101587","DOI":"10.1016\/j.jocs.2022.101587","volume":"59","author":"J Sun","year":"2022","unstructured":"Sun J, Wu S, Zhang H, Zhang X, Wang T (2022) Based on multi-algorithm hybrid method to predict the slope safety factor\u2013stacking ensemble learning with bayesian optimization. J Comput Sci 59:101587","journal-title":"J Comput Sci"},{"key":"1600_CR67","doi-asserted-by":"crossref","unstructured":"Torgo L, Ribeiro R (2009) Precision and Recall for Regression. Lecture Notes in Artificial Intelligence]. 12th International Conference on Discovery Science, Porto, Portugal","DOI":"10.1007\/978-3-642-04747-3_26"},{"key":"1600_CR68","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need.advances in neural information processing systems [Advances in neural information processing systems 30 (nips 2017)]. 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA."},{"issue":"11","key":"1600_CR69","doi-asserted-by":"publisher","first-page":"105413","DOI":"10.1016\/j.compgeo.2023.105413","volume":"159","author":"L Wang","year":"2023","unstructured":"Wang L, Wu CZ, Yang ZY, Wang LQ (2023) Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils. Comput Geotech 159(11):105413. https:\/\/doi.org\/10.1016\/j.compgeo.2023.105413","journal-title":"Comput Geotech"},{"key":"1600_CR70","doi-asserted-by":"publisher","first-page":"106544","DOI":"10.1016\/j.enggeo.2022.106544","volume":"298","author":"Y Wang","year":"2022","unstructured":"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 298:106544","journal-title":"Eng Geol"},{"issue":"10","key":"1600_CR71","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1061\/JSFEAQ.0001933","volume":"99","author":"SG Wright","year":"1973","unstructured":"Wright SG, Kulhawy FH, Duncan JM (1973) Accuracy of equilibrium slope stability analysis. J Soil Mech Found Div 99(10):783\u2013791. https:\/\/doi.org\/10.1061\/JSFEAQ.0001933","journal-title":"J Soil Mech Found Div"},{"key":"1600_CR72","doi-asserted-by":"publisher","unstructured":"Xi N, Yang Q, Sun YJ, Mei G (2023) Machine learning approaches for slope deformation prediction based on monitored time-series displacement data: a comparative investigation [Article]. Appl Sci-Basel, 13(8):19, Article 4677. https:\/\/doi.org\/10.3390\/app13084677","DOI":"10.3390\/app13084677"},{"issue":"6","key":"1600_CR73","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1002\/gj.4605","volume":"58","author":"WH Xu","year":"2023","unstructured":"Xu WH, Kang YF, Chen LCA, Wang LQ, Qin CB, Zhang LT, Liang D, Wu CZ, Zhang WG (2023) Dynamic assessment of slope stability based on multi-source monitoring data and ensemble learning approaches: a case study of Jiuxianping landslide. Geol J 58(6):2353\u20132371. https:\/\/doi.org\/10.1002\/gj.4605","journal-title":"Geol J"},{"issue":"2","key":"1600_CR74","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1007\/s12145-021-00571-z","volume":"14","author":"L Yan","year":"2021","unstructured":"Yan L, Chen CW, Hang TT, Hu YC (2021) A stream prediction model based on attention-LSTM. Earth Sci Inf 14(2):723\u2013733. https:\/\/doi.org\/10.1007\/s12145-021-00571-z","journal-title":"Earth Sci Inf"},{"key":"1600_CR75","doi-asserted-by":"publisher","first-page":"110066","DOI":"10.1016\/j.asoc.2023.110066","volume":"136","author":"W Zhang","year":"2023","unstructured":"Zhang W, Gu X, Hong L, Han L, Wang L (2023) Comprehensive review of machine learning in geotechnical reliability analysis: algorithms, applications and further challenges. Appl Soft Comput 136:110066","journal-title":"Appl Soft Comput"},{"issue":"4","key":"1600_CR76","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1016\/j.jrmge.2021.12.011","volume":"14","author":"W Zhang","year":"2022","unstructured":"Zhang W, Li H, Han L, Chen L, Wang L (2022a) Slope stability prediction using ensemble learning techniques: a case study in Yunyang County, Chongqing, China. J Rock Mech Geotech Eng 14(4):1089\u20131099","journal-title":"J Rock Mech Geotech Eng"},{"issue":"4","key":"1600_CR77","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1007\/s11440-022-01495-8","volume":"17","author":"W Zhang","year":"2022","unstructured":"Zhang W, Li H, Tang L, Gu X, Wang L, Wang L (2022b) Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks. Acta Geotech 17(4):1367\u20131382","journal-title":"Acta Geotech"},{"issue":"18","key":"1600_CR78","doi-asserted-by":"publisher","first-page":"104555","DOI":"10.1016\/j.autcon.2022.104555","volume":"143","author":"SZ Zhao","year":"2022","unstructured":"Zhao SZ, Kang F, Li JJ (2022) Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction. Autom Constr 143(18):104555. https:\/\/doi.org\/10.1016\/j.autcon.2022.104555","journal-title":"Autom Constr"},{"key":"1600_CR79","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.ssci.2019.05.046","volume":"118","author":"J Zhou","year":"2019","unstructured":"Zhou J, Li EM, Yang S, Wang MZ, Shi XZ, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505\u2013518. https:\/\/doi.org\/10.1016\/j.ssci.2019.05.046","journal-title":"Saf Sci"},{"key":"1600_CR80","doi-asserted-by":"publisher","unstructured":"Zhou J, Li XB, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civil Eng 30(5):19, 4016003. https:\/\/doi.org\/10.1061\/(asce)cp.1943-5487.0000553","DOI":"10.1061\/(asce)cp.1943-5487.0000553"},{"key":"1600_CR81","doi-asserted-by":"publisher","unstructured":"Zhou ML, Xing ZH, Nie C, Shi ZG, Hou B, Fu K (2022) Accurate prediction of tunnel face deformations in the rock tunnel construction process via high-granularity monitoring data and attention-based deep learning model. Appl Sci-Basel 12(19):18, Article 9523. https:\/\/doi.org\/10.3390\/app12199523","DOI":"10.3390\/app12199523"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01600-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01600-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01600-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:05:57Z","timestamp":1745654757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01600-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1600"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01600-3","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,11]]},"assertion":[{"value":"19 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have read and agreed to the published version of the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"30"}}