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Article

Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms

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Article: 2170832 | Received 05 Dec 2022, Accepted 17 Jan 2023, Published online: 25 Jan 2023

References

  • Ahmad ME, Sotirios AA, Moustafa MK, Lee VL, Fadzli MN. 2022. An integrated framework for the quantification of road network seismic vulnerability and accessibility to critical services. Sustainability. 14(12474):12474.
  • Alsabhan AH, Singh K, Sharma A, Alam S, Pandey DD, Rahman SAS, Khursheed A, Munshi FM. 2022. Landslide susceptibility assessment in the Himalayan range based along Kasauli–Parwanoo road corridor using weight of evidence, information value, and frequency ratio. J King Saud Univ-Sci. 34(2):101759.
  • Aminbakhsh S, Gunduz M, Sonmez R. 2013. Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. J Safety Res. 46:99–105.
  • Ayalew L, Yamagishi H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 65(1–2):15–31.
  • Bai S, Wang J, Lü G, Zhou P, Hou S, Xu S. 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology. 115(1–2):23–31.
  • Campbell WM, Campbell JP, Reynolds DA, Singer E, Torres-Carrasquillo PA. 2006. 2006. Support vector machines for speaker and language recognition. Comput Speech Language. 20(2–3):210–229.
  • Cengiz LD, Ercanoglu M. 2022. A novel data-driven approach to pairwise comparisons in AHP using fuzzy relations and matrices for landslide susceptibility assessments. Environ Earth Sci. 81(7):1–23.
  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S. 2017. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology. 297:69–85.
  • Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu AX. 2018. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena. 164:135–149. https://doi.org/10.1016/j.catena.2018.01.012.
  • Cui F. 2010. Study of traffic flow prediction based on BP neural network. In 2010 2nd International Workshop on Intelligent Systems and Applications, Wuhan, China. IEEE.
  • Das S, Sarkar S, Kanungo DP. 2022. GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya. Environ Monit Assess. 194(4):1–28.
  • Dong VD, Jaafari A, Bayat M, Gholami DM, Pham BT. 2020. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena. 188:104451.
  • Eidsvig UMK, McLean A, Vangelsten BV, Kalsnes B, Ciurean RL, Argyroudis S, Winter MG, Mavrouli OC, Fotopoulou S, Pitilakis K, et al. 2014. Assessment of socioeconomic vulnerability to landslides using an indicator-based approach: methodology and case studies. Bull Eng Geol Environ. 73(2):307–324.
  • Fanos AM, Pradhan B. 2019. A Spatial Ensemble Model for Rockfall Source Identification from High Resolution LiDAR Data and GIS. IEEE Access. 7:74570–74585.
  • Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T. 2014. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci. 73:208–221.
  • Guo Z, Chen W, Zhang J, Ye F, Liang X, He F, Guo Q. 2017. Hazard assessment of potentially dangerous bodies within a cliff based on the Fuzzy-AHP method: a case study of the Mogao Grottoes, China. Bull Eng Geol Environ. 76(3):1009–1020.
  • Hearn GJ, Pongpanya P. 2021. Developing a landslide vulnerability assessment for the national road network in Laos. QJEGH. 54(3).
  • Hong H, Pourghasemi HR, Pourtaghi ZS. 2016. Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology. 259:105–118.
  • Hosseini FS, Sigaroodi SK, Salajegheh A, Moghaddamnia A, Choubin B. 2021. Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories. Environ Sci Pollut Res. 28(44):62487–62498.
  • Hoque MA, Tasfia S, Ahmed N, Pradhan B. 2019. Assessing spatial flood vulnerability at Kalapara Upazila in Bangladesh using an analytic hierarchy process. Sensors-Basel. 19(6):1302.
  • Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science. 349(6245):255–260.
  • Kadi F, Yildirim F, Saralioglu E. 2021. Risk analysis of forest roads using landslide susceptibility maps and generation of the optimum forest road route: a case study in Macka, Turkey. Geocarto Int. 36(14):1612–1629.
  • Kahraman C, Cebeci U, Ruan D. 2004. Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey. Int J Prod Econ. 87(2):171–184.
  • Kahraman C, Cebeci U, Ulukan Z. 2003. Multi-criteria supplier selection using fuzzy AHP. Logistics information Management. 16(6):382–394.
  • Kakar SA, Sheikh N, Naseem A, Iqbal S, Rehman A, Ullah Kakar A, Kakar BA, Kakar HA, Khan B. 2018. Artificial neural network based weather prediction using Back Propagation Technique. IJACSA. 9(8):462–470.
  • Kim J, Lee S, Jung H, Lee S. 2018. Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int. 33(9):1000–1015.
  • Li CH, Kuo BC, Lin CT, Huang CS. 2012. A Spatial-Contextual Support Vector Machine for Remotely Sensed Image Classification. IEEE Trans Geosci Remote Sens. 50(3):784–799.
  • Lixin Y, Ke C, Xiaoying C, Yueling S, Xiaoqing C, Ye H. 2017. Analysis of social vulnerability of residential community to hazards in Tianjin. Nat Hazards. 87(2):1223–1243.
  • Lv L, Chen T, Dou J, Plaza A. 2022. A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping. Int J Appl Earth Obs. 108:102713.
  • Maletta R, Mendicino G. 2022. A methodological approach to assess the territorial vulnerability in terms of people and road characteristics. Georisk. 16:301–314.
  • Mandal K, Saha S, Mandal S. 2021. Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India. Geosci Front. 12(5):101203.
  • Marjanovi M, K, Evi M, B, Jat B, Enílek VV. 2011. Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol. 123:225–234.
  • MartínezCarvajal HE, de Moraes Guimarães Silva MT, García Aristizábal EF, Aristizábal-Giraldo EV, Larios Benavides MA. 2018. A mathematical approach for assessing landslide vulnerability. Earth Sci Res J. 22:251–273.
  • Petrucci O, Gullà G. 2010. A simplified method for assessing landslide damage indices. Nat Hazards. 52(3):539–560.
  • Pradhan B, Lee S. 2010a. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Modell Softw. 25(6):747–759.
  • Pradhan B, Lee S. 2010b. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides. 20107(1):13–30.
  • Pradhan B, Lee S, Buchroithner MF. 2010. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban. 34(3):216–235.
  • Qiu X, Zhang L, Suganthan PN, Amaratunga GA. 2017. Oblique random forest ensemble via least square estimation for time series forecasting. Inform Sci. 420:249–262.
  • Rajabi AM, Khodaparast M, Mohammadi M. 2022. Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran. Nat Hazards. 110(1):679–694.
  • Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. NATURE. 323(6088):533–536.
  • Saleh Y, Abolfazl J, Aleksandar V, Christopher G, Saskia K. 2022. Vulnerability assessment of road networks to landslide hazards in a dry-mountainous region. Environ Earth Sci. 81(22):1–17.
  • Saravanan S, Istijono B, Jennifer JJ, Abijith D, Sivaranjani S. 2021. Landslide susceptibility assessment using frequency ratio technique–A case study of NH67 road corridor in the Nilgiris district. Tamilnadu, India, Paper presented at IOP Conference Series: earth and Environmental Science, IOP Publishing, p. 012017.
  • Sitti AH, Hamizah AA, Nor EA, Mariyana AA, Nur SAS. 2022. Vulnerability of road transportation networks under natural hazards: a bibliometric analysis and review. Int J Disaster Risk Reduct. 83:103393.
  • Tehrany MS, Pradhan B, Jebur MN. 2014. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol. 512:332–343.
  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena. 118:124–135.
  • Wang Y, Sha A, Li X, Hao W. 2021. Prediction of the mechanical properties of titanium alloy castings based on a back-propagation neural network. J Mater Eng Perform. 30(11):8040–8047.
  • Xiong J, Sun M, Zhang H, Cheng W, Yang Y, Sun M, Cao Y, Wang J. 2019. Application of the Levenburg-Marquardt back propagation neural network approach for landslide risk assessments. Nat Hazards Earth Syst Sci. 19(3):629–653.
  • Xu K, Guo Q, Li Z, Xiao J, Qin Y, Chen D, Kong C. 2015. Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area. Int J Geogr Inf Sci. 29(7):1111–1124.
  • Zayed T, Amer M, Pan J. 2008. Assessing risk and uncertainty inherent in Chinese highway projects using AHP. Int J Proj Manag. 26(4):408–419.
  • Zhang Q, Yu H, Li Z, Zhang G, Ma DT. 2020. Assessing potential likelihood and impacts of landslides on transportation network vulnerability. Transport Res Part D 82:102304.
  • Zhang S, Zhang LM. 2014. Human vulnerability to quick shallow landslides along road: fleeing process and modeling. Landslides. 11(6):1115–1129.
  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR. 2018. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37.