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access icon openaccess Optimisation algorithm for decision trees and the prediction of horizon displacement of landslides monitoring

In the era of big data, the data scale of landslide monitoring could reach above terabyte level, the traditional database and data mining technology could no longer meet the requirements of intelligent monitoring and early warning. To obtain early warning information with high reliability and real time by applying big data theory, mechanisms, models and methods as well as machine learning methods are the inevitable trends in the future. This study aimed to realise a real time and precise mid-long prediction of landslide displacement, proposed two distributed landslide displacement prediction models: DLDP-GBTs (distributed landslide displacement prediction with Gradient Boosted Trees algorithm) and DLDP-RF (distributed landslide displacement prediction with Random algorithm); the cross-validation method was also adopted to evaluate and adjust parameters to reduce the root mean squared error of the model predicted results. In addition, this study proposed the rapid selection of features by using XGboost model in distributed situations can improve the Model training efficiency under distributed condition. By comparing different regression algorithms models, it was found that the DLDP-GBTs model based on the gradient optimisation decision tree was better than the other two models in terms of accuracy and real-time performance, which meets the requirements under the big data background.

References

    1. 1)
      • 8. Patri, A., Patnaik, Y.: ‘Random forest and stochastic gradient tree boosting based approach for the prediction of airfoil self-noise ⋆’, Procedia Comput. Sci., 2015, 46, pp. 109121.
    2. 2)
      • 18. Torlay, L., Perrone-Bertolotti, M., Thomas, E., et al: ‘Machine learning–XGboost analysis of language networks to classify patients with epilepsy’, Brain. Inform., 2017, 4, (3), pp. 159169.
    3. 3)
      • 11. Tan, F., Hu, X., He, C., et al: ‘Identifying the main control factors for different deformation stages of landslide’, Geotech. Geol. Eng., 2017, (2), pp. 114.
    4. 4)
      • 13. Xiao, R., He, X.: ‘Real-time landslide monitoring of pubugou hydropower resettlement zone using continuous gps’, Nat. Hazards, 2013, 69, (3), pp. 16471660.
    5. 5)
      • 3. Wu, X., Zhan, F.B., Zhang, K., et al: ‘Application of a two-step cluster analysis and the apriori algorithm to classify the deformation states of two typical colluvial landslides in the three gorges, China’, Environ. Earth Sci., 2016, 75, (2), pp. 146153.
    6. 6)
      • 2. Conte, E., Donato, A., Troncone, A.: ‘A simplified method for predicting rainfall-induced mobility of active landslides’, Landslides, 2016, 14, (1), pp. 111.
    7. 7)
      • 10. Pham, B.T., Bui, D.T., Prakash, I.: ‘Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study’, Geotech. Geol. Eng., 2017, 35, (6), pp. 25972611.
    8. 8)
      • 14. Xu, J.W., Liang, J.L.: ‘Research on distributed file system with hadoop’, Commun. Comput. Inf. Sci., 2012, 345, pp. 148155.
    9. 9)
      • 17. Tang, Z., Fu, Z., Gong, Z., et al: ‘A parallel conditional random fields model based on spark computing environment’, J. Grid Comput., 2017, 15, (3), pp. 120.
    10. 10)
      • 6. Salimi, A., Rostami, J., Moormann, C., et al: ‘Examining feasibility of developing a rock mass classification for hard rock tbm application using non-linear regression, regression tree and generic programming’, Geotech. Geol. Eng., 2017, 36, (2), pp. 11451159.
    11. 11)
      • 12. Zhang, Y., Hu, X., Tannant, D.D., et al: ‘Field monitoring and deformation characteristics of a landslide with piles in the three gorges reservoir area’, Landslides, 2018, 15, (3), pp. 581592.
    12. 12)
      • 16. Qiang, Y., Pei, B., Wu, W., et al: ‘Improvement of path analysis algorithm in social networks based on hbase’, J. Comb. Optim., 2014, 28, (3), pp. 588599.
    13. 13)
      • 7. Sachdeva, S., Bhatia, T., Verma, A.K.: ‘Gis-based evolutionary optimized gradient boosted decision trees for forest fire susceptibility mapping’, Nat. Hazards, 2018, 92, (3), pp. 13991418.
    14. 14)
      • 5. Mao, Y., Zhang, M., Sun, P., et al: ‘Landslide susceptibility assessment using uncertain decision tree model in loess areas’, Environ. Earth Sci., 2017, 76, (22), p. 752.
    15. 15)
      • 15. Zhang, M.Z., Meng-Liang, Y.U., Yong, W., et al: ‘Designing and building the national geo-environment monitoring data warehouse’, Earth Sci., 2013, 38, (6), pp. 13471355.
    16. 16)
      • 9. Naghibi, S.A., Ahmadi, K., Daneshi, A.: ‘Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping’, Water Res. Manage, 2017, 31, (9), pp. 115.
    17. 17)
      • 1. Duan, G., Niu, R., Ling, P., et al: ‘A landslide displacement prediction research based on optimization parameter ARIMA model under the inducing factors’, Geomatics Inf. Sci. Wuhan Univ., 2017, 42, (4), pp. 531536.
    18. 18)
      • 4. Caracciolo, D., Arnone, E., Conti, F.L., et al: ‘Exploiting historical rainfall and landslide data in a spatial database for the derivation of critical rainfall thresholds’, Environ. Earth Sci., 2017, 76, (5), p. 222.
    19. 19)
      • 19. Huang, T., Wang, Y., Wang, Z., et al: ‘Distributed traffic flow data prediction system based on spark’, Appl. Res. Comput., 2018, 35, (2), pp. 405416.
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