{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T12:58:11Z","timestamp":1781355491223,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T00:00:00Z","timestamp":1617667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based kinematic analysis. Results from the kinematic analysis, coupled with several commonly used landslide influencing factors, were adopted as input variables in ML models to predict landslides. In this paper, various ML models, such as random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and deep learning neural network (DLNN) models were evaluated. Results of two validation methods (confusion matrix and ROC curve) show that the involvement of discontinuity-related variables significantly improved the landslide predictive capability of these four models. Their addition demonstrated a minimum of 6% and 4% increase in the overall prediction accuracy and the area under curve (AUC), respectively. In addition, frequency ratio (FR) analysis showed good consistency between landslide probability that was characterized by FR values and discontinuity-related variables, indicating a high correlation. Both results of model validation and FR analysis highlight that inclusion of discontinuities into ML models can improve landslide prediction accuracy.<\/jats:p>","DOI":"10.3390\/ijgi10040232","type":"journal-article","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T21:44:47Z","timestamp":1617745487000},"page":"232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Maximizing Impacts of Remote Sensing Surveys in Slope Stability\u2014A Novel Method to Incorporate Discontinuities into Machine Learning Landslide Prediction"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5194-2071","authenticated-orcid":false,"given":"Lingfeng","family":"He","sequence":"first","affiliation":[{"name":"Camborne School of Mines, Penryn Campus, University of Exeter, Penryn TR10 9EZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7234-3588","authenticated-orcid":false,"given":"John","family":"Coggan","sequence":"additional","affiliation":[{"name":"Camborne School of Mines, Penryn Campus, University of Exeter, Penryn TR10 9EZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6358-374X","authenticated-orcid":false,"given":"Mirko","family":"Francioni","sequence":"additional","affiliation":[{"name":"Camborne School of Mines, Penryn Campus, University of Exeter, Penryn TR10 9EZ, UK"},{"name":"Department of Engineering and Geology, University of Chieti-Pescara, 66100 Chieti, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-674X","authenticated-orcid":false,"given":"Matthew","family":"Eyre","sequence":"additional","affiliation":[{"name":"Camborne School of Mines, Penryn Campus, University of Exeter, Penryn TR10 9EZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dilley, M., Chen, R., Deichmann, U., Lerner-Lam, A., Arnold, M., Agwe, J., and Yetman, G. (2005). Natural Disaster Hotspots: A Global Risk, The World Bank.","DOI":"10.1596\/0-8213-5930-4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1007\/s10346-018-0988-y","article-title":"Formation process of two massive dams following rainfall-induced deep-seated rapid landslide failures in the Kii Peninsula of Japan","volume":"15","author":"Sassa","year":"2018","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1007\/s10346-020-01533-0","article-title":"Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: An application to the Gold Basin landslide complex, northern Washington","volume":"18","author":"Xu","year":"2021","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1007\/s10346-020-01377-8","article-title":"Dynamic characteristics of high-elevation and long-runout landslides in the Emeishan basalt area: A case study of the Shuicheng \u201c7.23\u201dlandslide in Guizhou, China","volume":"17","author":"Gao","year":"2020","journal-title":"Landslides"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1007\/s00603-019-01963-w","article-title":"Geomechanical Field Survey to Identify an Unstable Rock Slope: The Passo della Morte Case History (NE Italy)","volume":"53","author":"Bolla","year":"2019","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s12665-009-0394-9","article-title":"Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine","volume":"61","author":"Yilmaz","year":"2009","journal-title":"Environ. Earth Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geomorph.2011.12.040","article-title":"GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China","volume":"145-146","author":"Xu","year":"2012","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10346-013-0391-7","article-title":"Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression","volume":"11","author":"Kavzoglu","year":"2013","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s11069-017-3104-z","article-title":"Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada","volume":"90","author":"Behnia","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.scitotenv.2018.01.124","article-title":"Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China","volume":"626","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping","volume":"191","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104426","DOI":"10.1016\/j.catena.2019.104426","article-title":"Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment","volume":"188","author":"Bui","year":"2020","journal-title":"Catena"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104249","DOI":"10.1016\/j.catena.2019.104249","article-title":"Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment","volume":"186","author":"Sameen","year":"2020","journal-title":"Catena"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-019-7968-0","article-title":"Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment","volume":"192","author":"Lucchese","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.gsf.2020.04.014","article-title":"Modelling of shallow landslides with machine learning algorithms","volume":"12","author":"Liu","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104458","DOI":"10.1016\/j.catena.2020.104458","article-title":"Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area","volume":"188","author":"Nhu","year":"2020","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104777","DOI":"10.1016\/j.catena.2020.104777","article-title":"GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models","volume":"195","author":"Chen","year":"2020","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.catena.2018.12.018","article-title":"Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches","volume":"175","author":"Pham","year":"2019","journal-title":"Catena"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jsg.2015.02.002","article-title":"A critical review of rock slope failure mechanisms: The importance of structural geology","volume":"74","author":"Stead","year":"2015","journal-title":"J. Struct. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1007\/s00603-016-1004-2","article-title":"Some Open Issues on Rockfall Hazard Analysis in Fractured Rock Mass: Problems and Prospects","volume":"49","author":"Ferrero","year":"2016","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jag.2017.12.016","article-title":"A combined field\/remote sensing approach for characterizing landslide risk in coastal areas","volume":"67","author":"Francioni","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1007\/s11069-017-3116-8","article-title":"Improvements in the integration of remote sensing and rock slope modelling","volume":"90","author":"Francioni","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.enggeo.2018.09.005","article-title":"Comparative study on dynamic shear behavior and failure mechanism of two types of granite joint","volume":"245","author":"Meng","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1007\/s11069-013-0982-6","article-title":"Probabilistic and sensitivity analyses of effective geotechnical parameters on rock slope stability: A case study of an urban area in northeast Iran","volume":"71","author":"Vatanpour","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.ijrmms.2015.06.008","article-title":"The possible role of brittle rock fracture in the 1963 Vajont Slide, Italy","volume":"78","author":"Havaej","year":"2015","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1007\/s10346-019-01192-w","article-title":"Investigation and modeling of direct toppling using a three-dimensional distinct element approach with incorporation of point cloud geometry","volume":"16","author":"Vanneschi","year":"2019","journal-title":"Landslides"},{"key":"ref_28","unstructured":"Shail, R.K., Coggan, J.S., and Stead, D. (1998, January 21\u201325). Coastal landsliding in Cornwall, UK: Mechanisms, modelling and implications. Proceedings of the 8th International Congress IAEG, Vancouver, BC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.pgeola.2011.03.004","article-title":"The Gramscatho Basin, south Cornwall, UK: Devonian active margin successions","volume":"122","author":"Leveridge","year":"2011","journal-title":"Proc. Geol. Assoc."},{"key":"ref_30","first-page":"191","article-title":"Devonian Rift-Related Sedimentation and Variscan Tectonics\u2014New Data on the Looe and Gramscatho Basins from the Resurvey of the Newquay District; The Ussher Society, 2006","volume":"11","author":"Hollick","year":"2006","journal-title":"Geosci. South-West Engl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.3390\/rs70201736","article-title":"Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)","volume":"7","author":"Turner","year":"2015","journal-title":"Remote. Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.isprsjprs.2015.06.008","article-title":"Tapping into the Hexagon spy imagery database: A new automated pipeline for geomorphic change detection","volume":"108","author":"Maurer","year":"2015","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1080\/15481603.2019.1687133","article-title":"Incorporating the effect of ALS-derived DEM uncertainty for quantifying changes due to the landslide in 2011, Mt. Umyeon, Seoul","volume":"57","author":"Kim","year":"2019","journal-title":"GIScience Remote. Sens."},{"key":"ref_34","unstructured":"(2021, February 06). Digimap. Available online: https:\/\/digimap.edina.ac.uk\/lidar."},{"key":"ref_35","unstructured":"(2016). Agisoft. Metashape, Agisoft LLC."},{"key":"ref_36","unstructured":"(2021, February 06). Channel Coastal Observatory. Available online: https:\/\/www.channelcoast.org\/."},{"key":"ref_37","unstructured":"Split Engineering LLC (2021, February 06). Split-FX. Available online: https:\/\/www.spliteng.com\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2113\/EEG-2042","article-title":"Rock Mass Characterization and Stability Evaluation of Mount Rushmore National Memorial, Keystone, South Dakota","volume":"24","author":"Poluga","year":"2018","journal-title":"Environ. Eng. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ijrmms.2008.04.007","article-title":"Optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses","volume":"46","author":"Lato","year":"2009","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.scitotenv.2019.02.263","article-title":"Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China","volume":"666","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shao, X., Ma, S., Xu, C., Zhang, P., Wen, B., Tian, Y., Zhou, Q., and Cui, Y. (2019). Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake. Remote. Sens., 11.","DOI":"10.3390\/rs11080978"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.renene.2014.10.048","article-title":"Coastal defence using wave farms: The role of farm-to-coast distance","volume":"75","author":"Abanades","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1111\/j.1365-3091.2006.00787.x","article-title":"The spatial and temporal variability of sand erosion across a stabilizing coastal dune field","volume":"53","author":"Levin","year":"2006","journal-title":"Sedimentology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1016\/j.gsf.2020.03.005","article-title":"Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process","volume":"11","author":"Pourghasemi","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_45","unstructured":"Hoek, E., and Brown, T. (1980). Underground Excavations in Rock, The Institution of Mining and Metallurgy. [1st ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10064-011-0384-5","article-title":"GIS-based kinematic slope instability and slope mass rating (SMR) maps: Application to a railway route in Sivas (Turkey)","volume":"71","author":"Yilmaz","year":"2011","journal-title":"Bull. Int. Assoc. Eng. Geol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hoek, E., and Bray, J.W. (1981). Rock Slope Engineering, The Institute of Mining and Metallurgy. [3rd ed.].","DOI":"10.1201\/9781482267099"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0013-7952(73)90013-6","article-title":"Review of a new shear-strength criterion for rock joints","volume":"7","author":"Barton","year":"1973","journal-title":"Eng. Geol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/4\/232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:43Z","timestamp":1760363923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/4\/232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,6]]},"references-count":49,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["ijgi10040232"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10040232","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,6]]}}}