Abstract
A novel interval prediction (PIs) method, called bootstrap based on generalize neural network (Bootstrap-GRNN) for landslide displacement forecasting model is proposed. New algorithm contains B+1 GRNN and then divide two parts. The first part includes B GRNN to compute variance. The second part has one GRNN to get variance of errors. According to the interval prediction formula, we can get the corresponding interval prediction for landslide displacement with real case.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cubito, A., Ferrara, V., Pappalardo, G.: Landslide hazard in the Nebrodi Mountains (northeastern Sicily). Geomorphology 66(1–4), 359–372 (2005)
Qin, S., Jiao, J., Wang, S.: The predictable time scale of landslides. Bull. Eng. Geol. Environ. 59(4), 307–312 (2001)
Qin, S.Q., Jiao, J., Wang, S.: A nonlinear dynamical model of landslide evolution. Geomorphology 43, 77–85 (2002)
Chen, J., Zeng, Z., Jiang, P., Tang, H.: Deformation prediction of landslide based on functional networtk. Neurocomputing 149, 151–157 (2015)
Lian, C., Zeng, Z., Yao, W., Tang, H.: Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat. Hazards 66, 759–771 (2013)
Khosravi, A., Mazloumi, E., Nahavandi, S., Creighton, D.: Prediction intervals to account for uncertainties in travel time prediction. IEEE Trans. Intell. Transp. Syst. 12(2), 537–547 (2011)
Sheng, C., Zhao, J., Wang, W., Leung, H.: Prediction intervals for a noisy nonlinear time series based on a bootstrapping reservoir computing network ensemble. IEEE Trans. Neural Netw. Learn. Syst. 24(7), 1036–1048 (2013)
Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.: Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans. Neural Netw. Learn. Syst. 24(3), 337–346 (2011)
Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979)
Efron, B., Tibshirani, R.: Bootstrap methods for standard errors, confidence intervals, and other measures of statixtical accuracy. Stat. Sci. 1(1), 54–77 (1986)
Efron, B.: The bootstrap and Markov chain Monte Carlo (2011)
Paojcic, J., Ibric, S., Djuric, Z., Milica, J., Owen, I.C.: An investigation into the usefulness of generalized regression neural network analysis in the developmen of level A in vitro-in vivo correlation. Eur. J. Pharm. Sci. 30(3), 264–272 (2007)
Acknowledgements
The work is supported by the Natural Science Foundation of China under Grant 61603129, the Natural Science Foundation of Hubei Province under Grant 2016CFC734.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chen, J., Zeng, Z., Jiang, P. (2017). Bootstrap Based on Generalized Regression Neural Network for Landslide Displacement for Interval Prediction. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-59072-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59071-4
Online ISBN: 978-3-319-59072-1
eBook Packages: Computer ScienceComputer Science (R0)