Landslides

pp 1–18 | Cite as

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

  • Ali Mutar Fanos
  • Biswajeet Pradhan
  • Shattri Mansor
  • Zainuddin Md Yusoff
  • Ahmad Fikri bin Abdullah
Original Paper
  • 33 Downloads

Abstract

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.

Keywords

Landslide Rockfall Machine learning LiDAR GIS Remote sensing 

References

  1. Abdulwahid WM, Pradhan B (2017) Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14(3):1057–1076.  https://doi.org/10.1007/s10346-016-0744-0
  2. Acosta E, Agliardi F, Crosta G B, Rıos Aragues S (2007) Regional rockfall hazard assessment in the Benasque Valley (Central Pyrenees) using a 3D numerical approach. In 4th EGS Plinius Conference–Mediterranean Storms, pp. 555–563Google Scholar
  3. Agliardi F, Riva F, Galletti L, Zanchi A Crosta G B (2016) Rockfall source characterization at high rock walls in complex geological settings by photogrammetry, structural analysis and DFN techniques. In EGU General Assembly Conference Abstracts 18, p. 13071Google Scholar
  4. Aksoy H, Ercanoglu M (2006) Determination of the rockfall source in an urban settlement area by using a rule-based fuzzy evaluation. Nat Hazard Earth Syst 6(6):941–954CrossRefGoogle Scholar
  5. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36CrossRefGoogle Scholar
  6. Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81:432–445CrossRefGoogle Scholar
  7. Bai SB, Lü G, Wang J, Zhou P, Ding L (2010) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62(1):139–149CrossRefGoogle Scholar
  8. Budetta P, De Luca C, Nappi M (2016) Quantitative rockfall risk assessment for an important road by means of the rockfall risk management (RO. MA.) method. Bull Eng Geol Environ 75(4):1377–1397CrossRefGoogle Scholar
  9. 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):361–378CrossRefGoogle Scholar
  10. Chen J, Zeng Z, Jiang P, Tang H (2015) Deformation prediction of landslide based on functional network. Neurocomputing 149:151–157CrossRefGoogle Scholar
  11. Chen Z, Gao B, Devereux B (2017) State-of-the-Art: DTM generation using airborne LIDAR data. Sensors 17(1):150CrossRefGoogle Scholar
  12. Cherkassky V, Mulier F M (2007) Learning from data: concepts, theory, and methods. 2nd edn. Wiley-IEEE Press, New JerseyGoogle Scholar
  13. Corominas J, Mavrouli O, Ruiz-Carulla R (2017) Magnitude and frequency relations: are there geological constraints to the rockfall size? Landslides 1–17.  https://doi.org/10.1007/s10346-017-0910-z
  14. Corona C, Trappmann D, Stoffel M (2013) Parameterization of rockfall source areas and magnitudes with ecological recorders: when disturbances in trees serve the calibration and validation of simulation runs. Geomorphology 202:33–42CrossRefGoogle Scholar
  15. Corona C, Lopez-Saez J, Favillier A, Mainieri R, Eckert N, Trappmann D, Stoffel M, Bourrier F, Berger F (2017) Modeling rockfall frequency and bounce height from three-dimensional simulation process models and growth disturbances in submontane broadleaved trees. Geomorphology 281:66–77CrossRefGoogle Scholar
  16. Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) 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. Nat Hazards 65(1):135–165CrossRefGoogle Scholar
  17. Evans JS, Hudak AT (2007) A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Trans Geosci Remote 45(4):1029–1038CrossRefGoogle Scholar
  18. Fanos AM, Pradhan B (2018) Laser scanning systems and techniques in rockfall source identification and risk assessment: a critical review. Earth Syst Environ, pp. 1–20.  https://doi.org/10.1007/s41748-018-0046-x
  19. Fanos AM, Pradhan B (2016) Multi-scenario rockfall hazard assessment using LiDAR data and GIS. Geotech Geol Eng 34(5):1375–1393CrossRefGoogle Scholar
  20. Fanos AM, Pradhan B, Aziz AA, Jebur MN, Park HJ (2016) Assessment of multi-scenario rockfall hazard based on mechanical parameters using high-resolution airborne laser scanning data and GIS in a tropical area. Environ Earth Sci 75(15):1129CrossRefGoogle Scholar
  21. Frattini P, Crosta G, Carrara A, Agliardi F (2008) Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology 94(3):419–437CrossRefGoogle Scholar
  22. Giacomini A, Ferrari F, Thoeni K, Lambert C (2016) A new rapid method for rockfall energies and distances estimation. In EGU General Assembly Conference Abstracts 18, p. 5323Google Scholar
  23. Gigli G, Morelli S, Fornera S, Casagli N (2014) Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios. Landslides 11(1):1–14CrossRefGoogle Scholar
  24. Günther A, Carstensen A, Pohl W (2004) Automated sliding susceptibility mapping of rock slopes. Nat Hazards Earth Syst Sci 4(1):95–102CrossRefGoogle Scholar
  25. Guzzetti F, Crosta G, Detti R, Agliardi F (2002) STONE: a computer program for the three-dimensional simulation of rock-falls. Comput Geosci 28(9):1079–1093CrossRefGoogle Scholar
  26. Guzzetti F, Reichenbach P, Wieczorek GF (2003) Rockfall hazard and risk assessment in the Yosemite Valley, California, USA. Nat Hazards Earth Syst Sci 3(6):491–503CrossRefGoogle Scholar
  27. Jaboyedoff M, Labiouse V (2003) Preliminary assessment of rockfall hazard based on GIS data. In 10th ISRM Congress. International Society for Rock MechanicsGoogle Scholar
  28. Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Haz Risk 9(1):49–69CrossRefGoogle Scholar
  29. Krummenacher B (1995) Modellierung der Wirkungsräume von Erd-und Felsbewegungen mit Hilfe Geographischer Informationssysteme (GIS). Schweiz Z Forstwes 146:741–761Google Scholar
  30. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491CrossRefGoogle Scholar
  31. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41CrossRefGoogle Scholar
  32. Losasso L, Jaboyedoff M, Sdao F (2017) Potential rock fall source areas identification and rock fall propagation in the province of Potenza territory using an empirically distributed approach. Landslides 14(5):1593–1602CrossRefGoogle Scholar
  33. Loye A, Jaboyedoff M, Pedrazzini A (2009) Identification of potential rockfall source areas at a regional scale using a DEM-based geomorphometric analysis. Nat Hazards Earth Syst Sci 9(5):1643–1653CrossRefGoogle Scholar
  34. Macciotta R, Hendry M, Cruden DM, Blais-Stevens A, Edwards T (2017) Quantifying rock fall probabilities and their temporal distribution associated with weather seasonality. Landslides 14(6):2025–2039CrossRefGoogle Scholar
  35. Massey CI, McSaveney MJ, Taig T, Richards L, Litchfield NJ, Rhoades DA et al (2014) Determining rockfall risk in Christchurch using rockfalls triggered by the 2010–2011 Canterbury earthquake sequence. Earthquake Spectra 30(1):155–181CrossRefGoogle Scholar
  36. Matasci B, Stock G M, Jaboyedoff M, Carrea D, Collins B D, Guérin A, ... , Ravanel L (2017) Assessing rockfall susceptibility in steep and overhanging slopes using three-dimensional analysis of failure mechanisms. Landslides 1–20Google Scholar
  37. Meissl G (1998) Modellierung der Reichweite von Felsstürzen: Fallbeispiele zur GIS-gestützten Gefahrenbeurteilung (Doctoral dissertation, Ph. D. thesis, Institut für Geographie. Univ. Innsbruck)Google Scholar
  38. Messenzehl K, Meyer H, Otto JC, Hoffmann T, Dikau R (2017) Regional-scale controls on the spatial activity of rockfalls (Turtmann valley, Swiss Alps)—a multivariate modeling approach. Geomorphology 287:29–45CrossRefGoogle Scholar
  39. Mezaal MR, Pradhan B (2018) Data mining-aided automatic landslide detection using airborne laser scanning data in densely forested tropical areas. Korean Journal of Remote Sensing, 34(1):45–74.  https://doi.org/10.7780/kjrs.2018.34.1.4
  40. Mongus D, Zalik B (2014) Computationally efficient method for the generation of a digital terrain model from airborne LiDAR data using connected operators. IEEE J Sel Top 7(1):340–351Google Scholar
  41. Pellicani R, Spilotro G, Van Westen CJ (2016) Rockfall trajectory modeling combined with heuristic analysis for assessing the rockfall hazard along the Maratea SS18 coastal road (Basilicata, Southern Italy). Landslides 13(5):985–1003CrossRefGoogle Scholar
  42. Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250CrossRefGoogle Scholar
  43. Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320CrossRefGoogle Scholar
  44. Pradhan B (2011a) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349CrossRefGoogle Scholar
  45. Pradhan B (2011b) Manifestation of an advanced fuzzy logic model coupled with Geoinformation techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18(3):471–493CrossRefGoogle Scholar
  46. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365CrossRefGoogle Scholar
  47. Pradhan B, Fanos A M (2017a) Rockfall hazard assessment: an overview. In Laser Scanning Applications in Landslide Assessment (pp. 299–322). Springer, Cham. doi.org/10.1007/978-3-319-55342-9_15
  48. Pradhan B, Fanos A M (2017b) Application of LiDAR in rockfall hazard assessment in tropical region. In Laser Scanning Applications in Landslide Assessment (pp. 323–359). Springer, Cham. doi.org/10.1007/978-3-319-55342-9_16
  49. Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054CrossRefGoogle Scholar
  50. Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177CrossRefGoogle Scholar
  51. Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73(2):1019–1042CrossRefGoogle Scholar
  52. Pradhan B, Seeni M I, Nampak H (2017) Integration of LiDAR and QuickBird data for automatic landslide detection using object-based analysis and random forests. In: Pradhan B (ed) Laser scanning applications in landslide assessment, Springer, Cham, pp. 69–81.  https://doi.org/10.1007/978-3-319-55342-9_4
  53. Prasad R, Pandey A, Singh KP, Singh VP, Mishra RK, Singh D (2012) Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: a comparison of different transfer functions. Adv Space Res 50(3):363–370CrossRefGoogle Scholar
  54. Reynolds D (2015) Gaussian mixture models. Encyclopedia of Biometrics 827–832Google Scholar
  55. Sameen MI, Pradhan B (2017) A novel road segmentation technique from orthophotos using deep convolutional autoencoders. Korean Journal of Remote Sensing 33(4):423–436.  https://doi.org/10.7780/kjrs.2017.33.4.8
  56. Schicker R, Moon V (2012) Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161:40–57CrossRefGoogle Scholar
  57. Varnes DJ (1978) Slope movement types and processes. Spec Rep 176:11–33Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia
  2. 2.School of Systems, Management and Leadership, Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia
  3. 3.Department of Energy and Mineral Resources EngineeringChoongmu-gwan, Sejong UniversitySeoulSouth Korea
  4. 4.Department of Biological and Agricultural Engineering, Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia

Personalised recommendations