Landslide Risk Analysis Using Machine Learning Principles: A Case Study of Bukit Antrabangsa Landslide Incidence
Abstract
A quantitative analysis of landslides was carried out in this research to ascertain the risk that will be incurred on housing development built along hillside slopes. The study uses results from the most recent landslides analysis of GIS data and soft computing techniques to describe a concept for determining risk in a housing development situated on the hillside. The concepts presented by this study are timely, which means that computations define the time in which the housing development becomes vulnerable to landslides. The general idea enclosed in this research was to merge recent landslides analysis with real-life situations. The study location for this research was an already developed area situated along the hillside slopes of Bukit Antrabangsa, Malaysia. The approach includes predicting landslides using a novel machine-learning algorithm ensemble, the random subspace technique. The prediction process relates to the probability of occurrence of the slides responsible for the vulnerability of the housing development. A landslides prediction technique that employs GIS data to develop a geospatial database and machine learning algorithms for predicting future occurrences was used to produce the landslides inventory. The study area’s landslide inventory was then used as reference locations to predict landslide occurrence under the influence of ten landslide predisposing factors. Prediction results were evaluated for accuracy using the ROC (receiver operator characteristics) and calculated the AUC (area under the curve). Other parameters used to decide the quality of the models include the RSME (root-mean-square error), the MAE (mean absolute error), and the F-measure. The results obtained from the statistical analysis of the model show that the model has high predictive and success rates. The soft computing results now gave ways to obtain a timely probability of occurrence of the slides using a deterministic approach. The analysis was concluded by providing a conceptual equation to determine the timely probability of the slides using the available hazard information.
Keywords: landslide risk mapping, random subspace, failure, machine learning, risk analysis, hillside development.
Full Text:
PDFReferences
LOMBARDO L., OPITZ T., ARDIZZONE F., GUZZETTI F., and HUSER R. Space-time landslide predictive modelling. Earth-Science Reviews, 2020, 209: 103318. https://doi.org/10.1016/j.earscirev.2020.103318
ALFIERI L., SALAMON P., PAPPENBERGER F., WETTERHALL F., and THIELEN J. Operational early warning systems for water-related hazards in Europe. Environmental Science & Policy, 2012, 21: 35–49. https://doi.org/10.1016/j.envsci.2012.01.008
HONG Y. and ADLER R. F. Towards an early-warning system for global landslides triggered by rainfall and earthquake. International Journal of Remote Sensing, 2007, 28(16): 3713–3719. https://doi.org/10.1080/01431160701311242
NAIDU S., SAJINKUMAR K. S., OOMMEN T., ANUJA V. J., SAMUEL R. A., and MURALEEDHARAN C. Early warning system for shallow landslides using rainfall threshold and slope stability analysis. Geoscience Frontiers, 2018, 9(6): 1871–1882. https://doi.org/10.1016/j.gsf.2017.10.008
PICIULLO L., CALVELLO M., and CEPEDA J. M. Territorial early warning systems for rainfall-induced landslides. Earth-Science Reviews, 2018, 179: 228–247. https://doi.org/10.1016/j.earscirev.2018.02.013
THONGS, G. Integrating risk perceptions into flood risk management: Trinidad case study. Natural Hazards, 2019, 98(2): 593–619. https://doi.org/10.1007/s11069-019-03720-2
HEGDE J. and ROKSETH B. Applications of machine learning methods for engineering risk assessment – A review. Safety Science, 2020, 122: 104492. https://doi.org/10.1016/j.ssci.2019.09.015
PARRY S. and NG K. C. The assessment of landslide risk from natural slopes in Hong Kong: An engineering geological perspective. Quarterly Journal of Engineering Geology and Hydrogeology, 2010, 43(3): 307–320. http://dx.doi.org/10.1144/1470-9236/08-012
LI D. Q., DING Y. N., TANG X. S., and LIU Y. Probabilistic risk assessment of landslide-induced surges considering the spatial variability of soils. Engineering Geology, 2021, 283: 105976. https://doi.org/10.1016/j.enggeo.2020.105976
BAI Q. and BAI Y. Finite Element Analysis of In Situ Behavior. In: Subsea Pipeline Design, Analysis, and Installation. Elsevier, 2014: 171–185. https://doi.org/10.1016/B978-0-12-386888-6.00008-0
GRIFFITHS D. V. and FENTON G. A. Probabilistic slope stability analysis by finite elements. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(5): 507–518. https://doi.org/10.1061/%28ASCE%291090-0241%282004%29130%3A5%28507%29
CHEN K., ZOU D., KONG X., CHAN A., and HU Z. A novel nonlinear solution for the polygon scaled boundary finite element method and its application to geotechnical structures. Computers and Geotechnics, 2017, 82: 201–210. https://doi.org/10.1016/j.compgeo.2016.09.013
HAN L., MA Q., ZHANG F., ZHANG Y., ZHANG J., BAO Y., and ZHAO J. Risk assessment of an earthquake-collapse-landslide disaster chain by bayesian network and newmark models. International Journal of Environmental Research and Public Health, 2019, 16(18): 3330. https://doi.org/10.3390/ijerph16183330
LEE S. and PRADHAN B. Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science, 2006, 115(6): 661–672. https://doi.org/10.1007/s12040-006-0004-0
RAMADAN S. S. R. and NAGHI H. A GIS-based landslide hazard framework for road repair and maintenance. Electronic Journal of Geotechnical Engineering, 2005.
PRIYONO K. D. Risk Analysis of Landslide Impacts on Settlements in Karanganyar, Central Java, Indonesia. International Journal of GEOMATE, 2020, 19(73): 100–107. https://doi.org/10.21660/2020.73.34128
KADI F., YILDIRIM F., and SARALIOGLU E. Risk analysis of forest roads using landslide susceptibility maps and generation of the optimum forest road route: a case study in Macka, Turkey. Geocarto International, 2021, 36(14): 1612-1629. https://doi.org/10.1080/10106049.2019.1659424
LIU X. and WANG Y. Probabilistic simulation of entire process of rainfall-induced landslides using random finite element and material point methods with hydro-mechanical coupling. Computers and Geotechnics, 2021, 132: 103989. https://doi.org/10.1016/j.compgeo.2020.103989
SHI Z. M., XIONG X., PENG M., ZHANG L. M., XIONG Y. F., CHEN H. X., and ZHU Y. Risk assessment and mitigation for the Hongshiyan landslide dam triggered by the 2014 Ludian earthquake in Yunnan, China. Landslides, 2017, 14(1): 269–285. https://doi.org/10.1007/s10346-016-0699-1
SHAHAROM S., HUAT L. T., and OTHMAN M. A. Area Based Landslide Hazard and Risk Assessment for Penang Island Malaysia. In: SASSA K., CANUTI P., and YIN Y. (eds.) Landslide Science for a Safer Geoenvironment. Springer, Cham, 2014: 513–519. https://doi.org/10.1007/978-3-319-05050-8_79
MAES J. W., KERVYN M., DE HONTHEIM A., DEWITTE O., JACOBS L., MERTENS K., VANMAERCKE M., VRANKEN L., and POESEN J. Landslide risk reduction measures: A review of practices and challenges for the tropics. Progress in Physical Geography, 2017, 41(2): 191–221. https://doi.org/10.1177/0309133316689344
POPESCU M. E. and TRANDAFIR A. C. Landslide risk assessment and mitigation. In: CHEN W.-F. and DUAN L. (eds.) Bridge Engineering Handbook: Substructure Design. CRC Press, Boca Raton, Florida, 2014: 315–359. https://www.taylorfrancis.com/chapters/edit/10.1201/b15621-16/landslide-risk-assessment-mitigation-mihail-popescu-aurelian-trandafir?context=ubx&refId=0d256964-b029-4f11-b26d-4efb70103503
ZÊZERE J. L., GARCIA R. A. C., OLIVEIRA S. C., and REIS E. Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon (Portugal). Geomorphology, 2008, 94(3–4): 467–495. https://doi.org/10.1016/j.geomorph.2006.10.040
KAZMI D., QASIM S., HARAHAP I. S. H., and VU T. H. Analytical study of the causes of the major landslide of Bukit Antarabangsa in 2008 using fault tree analysis. Innovative Infrastructure Solutions, 2017, 2(1): 55. https://doi.org/10.1007/s41062-017-0105-4
HUAT L. T. and IBRAHIM A. S. An Investigation on One of the Rainfall-Induced Landslides in Malaysia. Electronic Journal of Geotechnical Engineering, 2012, 17: 435-449.
LAM K. An Assessment of Current Practices on Landslides Risk Management: A Case of Kuala Lumpur Territory. Geografia: Malaysian Journal of Society and Space, 2017, 13(2): 1–12. https://ejournal.ukm.my/gmjss/article/view/17853
QURAISHI I., HASNAT A., and CHOUDHURY J. P. Selection of optimal pixel resolution for landslide susceptibility analysis within the Bukit Antarabangsa, Kuala Lumpur, by using image processing and multivariate statistical tools. EURASIP Journal on Image and Video Processing, 2017, 2017(1): 21. https://doi.org/10.1186/s13640-017-0169-2
ALTHUWAYNEE O. F., PRADHAN B., and AHMAD N. Estimation of rainfall threshold and its use in landslide hazard mapping of Kuala Lumpur metropolitan and surrounding areas. Landslides, 2015, 12(5): 861–875. https://doi.org/10.1007/s10346-014-0512-y
ABDULLAH A. F., AIMRUN W., NASIDI N. M., HAZARI K., SIDEK L. M., and SELAMAT Z. Modelling erosion and landslides induced by farming activities at Hilly Areas, Cameron Highlands, Malaysia. Jurnal Teknologi, 2019, 81(6): 195–204. https://doi.org/10.11113/jt.v81.13795
SAADATKHAH N., KASSIM A., and LEE L. M. Qualitative and quantitative landslide susceptibility assessments in Hulu Kelang area, Malaysia. Electronic Journal of Geotechnical Engineering, 2014, 19(C): 545–563. https://www.researchgate.net/profile/Nader-Saadatkhah/publication/286196305_Qualitative_and_quantitative_landslide_susceptibility_assessments_in_Hulu_Kelang_area_Malaysia/links/5916a13ba6fdcc963e83e8e8/Qualitative-and-quantitative-landslide-susceptibility-assessments-in-Hulu-Kelang-area-Malaysia.pdf
MANAP M. A., RAMLI M. F., AZMIN SULAIMAN W. N., and SURIP N. Application of remote sensing in the identification of the geological terrain features in Cameron highlands, Malaysia. Sains Malaysiana, 2010, 39(1): 1–11. http://www.ukm.my/jsm/pdf_files/SM-PDF-39-1-2010/01.pdf
IBRAHIM M. B. and ISKANDAR P. S. Geospatial Data Preparation for Deep Learning Inference System, n.d.
FU S., CHEN L., WOLDAI T., YIN K., GUI L., LI D., DU J., ZHOU C., XU Y., and LIAN Z. Landslide hazard probability and risk assessment at the community level: A case of western Hubei, China. Natural Hazards and Earth System Sciences, 2020, 20(2): 581–601. https://doi.org/10.5194/nhess-20-581-2020
VAN WESTEN C. Geo-Information tools for landslide risk assessment: an overview of recent developments. In: MAURICIO EHRLICH W. L., FONTOURA S. A. B., and SAYAO A. S. F. (eds.) Proceedings of the Ninth International Symposium on Landslides "Landslides: Evaluation and Stabilization," Vol. 1. Balkema, Taylor & Francis Group, London, 2004: 39–56. https://books.google.com/books?hl=ru&lr=&id=DLPNBQAAQBAJ&oi=fnd&pg=PA39&ots=qsTtOY0P5e&sig=zVRjmzf9dQaHEGDSc0RjnOZD0gE#v=onepage&q&f=false
CHEN W., YAN X., ZHAO Z., HONG H., BUI D. T., and PRADHAN B. Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bulletin of Engineering Geology and the Environment, 2019, 78(1): 247–266. https://doi.org/10.1007/s10064-018-1256-z
DEVKOTA K. C., REGMI A. D., POURGHASEMI H. R., YOSHIDA K., PRADHAN B., RYU I. C., DHITAL M. R., and ALTHUWAYNEE O. F. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 2013, 65(1): 135–165. https://doi.org/10.1007/s11069-012-0347-6
VAKHSHOORI V., POURGHASEMI H. R., ZARE M., and BLASCHKE T. Landslide susceptibility mapping using GIS-based data mining algorithms. Water, 2019, 11(11): 7–13. https://doi.org/10.3390/w11112292
IBRAHIM M. B., MUSTAFFA Z., BALOGUN A.-L., HAMONANGAN HARAHAP I. S., and ALI KHAN M. Advanced data mining techniques for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 2021, 12(1): 2430–2461. https://doi.org/10.1080/19475705.2021.1960433
INTERNATIONAL RESOURCE JOURNAL. The Sabah Sarawak Integrated Oil and Gas Project, 2010.
THE MALAYSIAN SOCIETY OF SOIL SCIENCE. Characteristics of some soils in Sabah and Sarawak, 1977.
BONG Y. S., ZULKIFLY M. H., SATI I., and HARAHAP H. GIS Analysis & Landslide Susceptibility Mapping (LSM) in Murum Reservoir Region, Sarawak. International Journal of Engineering & Technology, 2018, 7(3.7): 456–459. http://dx.doi.org/10.14419/ijet.v7i3.7.18905
TIEN BUI D., HO T. C., PRADHAN B., PHAM B. T., NHU V. H., and REVHAUG I. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environmental Earth Sciences, 2016, 75(14): 1101. https://doi.org/10.1007/s12665-016-5919-4
ZÊZERE J. L., DE BRUM FERREIRA A., and RODRIGUES M. L. The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal). Geomorphology, 1999, 30(1–2): 133–146. https://doi.org/10.1016/S0169-555X(99)00050-1
POURGHASEMI H. R., TEIMOORI YANSARI Z., PANAGOS P., and PRADHAN B. 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, 2018, 11(9): 193. https://doi.org/10.1007/s12517-018-3531-5
SHIN J. Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces. Frontiers in Human Neuroscience, 2020, 14: 236. https://doi.org/10.3389/fnhum.2020.00236
GHOLAMI H., MOHAMMADIFAR A., POURGHASEMI H. R., and COLLINS A. L. A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust. Environmental Science and Pollution Research, 2020, 27(33): 42022–42039. https://doi.org/10.1007/s11356-020-10168-6
ZHANG K., WU X., NIU R., YANG K., and ZHAO L. The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environmental Earth Sciences, 2017, 76(11): 405. https://doi.org/10.1007/s12665-017-6731-5
GOETZ J. N., BRENNING A., PETSCHKO H., and LEOPOLD P. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences, 2015, 81: 1–11. https://doi.org/10.1016/j.cageo.2015.04.007
PAL M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 2005, 26(1): 217–222. https://doi.org/10.1080/01431160412331269698
CHEN W., XIE X., PENG J., WANG J., DUAN Z., and HONG H. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomatics, Natural Hazards and Risk, 2017, 8(2): 950–973. https://doi.org/10.1080/19475705.2017.1289250
LI C. Q. and ZHAO J. M. Time-dependent risk assessment of combined overtopping and structural failure for reinforced concrete coastal structures. Journal of Waterway, Port, Coastal, and Ocean Engineering, 2010, 136(2): 97–103. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000031
WU T. H. Risk and reliability in geotechnical engineering. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2015, 9(3): 218-219. https://doi.org/10.1080/17499518.2015.1070784
ACHOUR Y. and POURGHASEMI H. R. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 2020, 11(3): 871–883. https://doi.org/10.1016/j.gsf.2019.10.001
PALLADINO M. R., VIERO A., TURCONI L., BRUNETTI M., PERUCCACCI S., MELILLO M., LUINO F., DEGANUTTI A. M., and GUZZETTI F. Rainfall thresholds for the activation of shallow landslides in the Italian Alps: the role of environmental conditioning factors. Geomorphology, 2018, 303: 53–67. https://doi.org/10.1016/j.geomorph.2017.11.009
TEPEL R. E. Landslides: Investigation and Mitigation. Environmental & Engineering Geoscience, 1998, 4(2): 277–278. https://doi.org/10.2113/gseegeosci.iv.2.277
PHAM B. T., TIEN BUI D., POURGHASEMI H. R., INDRA P., and DHOLAKIA M. B. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology, 2017, 128(1–2): 255–273. https://doi.org/10.1007/s00704-015-1702-9
GUZZETTI F., REICHENBACH P., ARDIZZONE F., CARDINALI M., and GALLI M. Estimating the quality of landslide susceptibility models. Geomorphology, 2006, 81(1–2): 166–184. https://doi.org/10.1016/j.geomorph.2006.04.007
COLLINS B. D. and ZNIDARCIC D. Stability Analyses of Rainfall Induced Landslides. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(4): 362–372. https://doi.org/10.1061/%28ASCE%291090-0241%282004%29130%3A4%28362%29
WONG C. L., LIEW J., YUSOP Z., ISMAIL T., VENNEKER R., and UHLENBROOK S. Rainfall characteristics and regionalization in peninsular malaysia based on a high resolution gridded data set. Water, 2016, 8(11): 500. https://doi.org/10.3390/w8110500
RAHMATI O., TAHMASEBIPOUR N., HAGHIZADEH A., POURGHASEMI H. R., and FEIZIZADEH B. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 2017, 298: 118–137. https://doi.org/10.1016/j.geomorph.2017.09.006
KAVZOGLU T., SAHIN E. K., and COLKESEN I. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 2014, 11(3): 425–439. https://doi.org/10.1007/s10346-013-0391-7
TSANGARATOS P. and ILIA I. Applying Machine Learning Algorithms in Landslide Susceptibility Assessments. In: SAMUI P., SEKHAR S., and BALAS V. E. (eds.) Handbook of Neural Computation, 1st ed. Elsevier, 2017: 433-457. https://doi.org/10.1016/B978-0-12-811318-9.00024-7
Refbacks
- There are currently no refbacks.
