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

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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.

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References

  • 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

  • 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. 555–563

  • 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. 13071

  • 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):941–954

    Article  Google Scholar 

  • 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 map**. Catena 114:21–36

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility map** with comparisons of results from two methods and verifications. Eng Geol 81:432–445

    Article  Google Scholar 

  • Bai SB, Lü G, Wang J, Zhou P, Ding L (2010) GIS-based rare events logistic regression for landslide-susceptibility map** of Lianyungang, China. Environ Earth Sci 62(1):139–149

    Article  Google Scholar 

  • 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):1377–1397

    Article  Google Scholar 

  • 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):361–378

    Article  Google Scholar 

  • Chen J, Zeng Z, Jiang P, Tang H (2015) Deformation prediction of landslide based on functional network. Neurocomputing 149:151–157

    Article  Google Scholar 

  • Chen Z, Gao B, Devereux B (2017) State-of-the-Art: DTM generation using airborne LIDAR data. Sensors 17(1):150

    Article  Google Scholar 

  • Cherkassky V, Mulier F M (2007) Learning from data: concepts, theory, and methods. 2nd edn. Wiley-IEEE Press, New Jersey

  • 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

  • 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 202:33–42

    Article  Google Scholar 

  • 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 281:66–77

    Article  Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility map** 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):135–165

    Article  Google Scholar 

  • Evans JS, Hudak AT (2007) A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Trans Geosci Remote 45(4):1029–1038

    Article  Google Scholar 

  • 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

  • Fanos AM, Pradhan B (2016) Multi-scenario rockfall hazard assessment using LiDAR data and GIS. Geotech Geol Eng 34(5):1375–1393

    Article  Google Scholar 

  • 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):1129

    Article  Google Scholar 

  • Frattini P, Crosta G, Carrara A, Agliardi F (2008) Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology 94(3):419–437

    Article  Google Scholar 

  • 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. 5323

  • 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):1–14

    Article  Google Scholar 

  • Günther A, Carstensen A, Pohl W (2004) Automated sliding susceptibility map** of rock slopes. Nat Hazards Earth Syst Sci 4(1):95–102

    Article  Google Scholar 

  • 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):1079–1093

    Article  Google Scholar 

  • 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):491–503

    Article  Google Scholar 

  • Jaboyedoff M, Labiouse V (2003) Preliminary assessment of rockfall hazard based on GIS data. In 10th ISRM Congress. International Society for Rock Mechanics

  • Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility map**: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Haz Risk 9(1):49–69

    Article  Google Scholar 

  • Krummenacher B (1995) Modellierung der Wirkungsräume von Erd-und Felsbewegungen mit Hilfe Geographischer Informationssysteme (GIS). Schweiz Z Forstwes 146:741–761

    Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility map** using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard map** at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41

    Article  Google Scholar 

  • 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):1593–1602

    Article  Google Scholar 

  • 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):1643–1653

    Article  Google Scholar 

  • 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):2025–2039

    Article  Google Scholar 

  • 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):155–181

    Article  Google Scholar 

  • 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 1–20

  • Meissl G (1998) Modellierung der Reichweite von Felsstürzen: Fallbeispiele zur GIS-gestützten Gefahrenbeurteilung (Doctoral dissertation, Ph. D. thesis, Institut für Geographie. Univ. Innsbruck)

  • 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 287:29–45

    Article  Google Scholar 

  • 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

  • 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):340–351

    Google Scholar 

  • 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):985–1003

    Article  Google Scholar 

  • 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 84:240–250

    Article  Google Scholar 

  • Pradhan B (2010) Landslide susceptibility map** of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320

    Article  Google Scholar 

  • 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):329–349

    Article  Google Scholar 

  • Pradhan B (2011b) Manifestation of an advanced fuzzy logic model coupled with Geoinformation techniques to landslide susceptibility map** and their comparison with logistic regression modelling. Environ Ecol Stat 18(3):471–493

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility map** using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • 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

  • 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

  • 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):1037–1054

    Article  Google Scholar 

  • Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility map** by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177

    Article  Google Scholar 

  • Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility map** at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73(2):1019–1042

    Article  Google Scholar 

  • 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

  • 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):363–370

    Article  Google Scholar 

  • Reynolds D (2015) Gaussian mixture models. Encyclopedia of Biometrics 827–832

  • 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

  • Schicker R, Moon V (2012) Comparison of bivariate and multivariate statistical approaches in landslide susceptibility map** at a regional scale. Geomorphology 161:40–57

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. Spec Rep 176:11–33

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Ali Mutar Fanos, Shattri Mansor & Zainuddin Md Yusoff

  2. School of Systems, Management and Leadership, Faculty of Engineering and Information Technology, University of Technology Sydney, Building 11, Level 06, 81 Broadway, PO Box 123, Ultimo, NSW, 2007, Australia

    Biswajeet Pradhan

  3. Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro Gwang**-gu, 05006, Seoul, South Korea

    Biswajeet Pradhan

  4. Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Ahmad Fikri bin Abdullah

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  1. Ali Mutar Fanos
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Correspondence to Biswajeet Pradhan.

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Fanos, A.M., Pradhan, B., Mansor, S. et al. A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data. Landslides 15, 1833–1850 (2018). https://doi.org/10.1007/s10346-018-0990-4

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