Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights

In this paper, we propose a new approach to establish a landslide displacement forecasting model based on artificial neural networks (ANNs) with random hidden weights. To quantify the uncertainty associated with the predictions, a framework for probabilistic forecasting of landslide displacement is developed. The aim of this paper is to construct prediction intervals (PIs) instead of deterministic forecasting. A lower-upper bound estimation (LUBE) method is adopted to construct ANN-based PIs, while a new single hidden layer feedforward ANN with random hidden weights for LUBE is proposed. Unlike the original implementation of LUBE, the input weights and hidden biases of the ANN are randomly chosen, and only the output weights need to be adjusted. Combining particle swarm optimization (PSO) and gravitational search algorithm (GSA), a hybrid evolutionary algorithm, PSOGSA, is utilized to optimize the output weights. Furthermore, a new ANN objective function, which combines a modified combinational coverage width-based criterion with one-norm regularization, is proposed. Two benchmark data sets and two real-world landslide data sets are presented to illustrate the capability and merit of our method. Experimental results reveal that the proposed method can construct high-quality PIs.

[1]  Cheng LianZhigang,et al.  Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine , 2013 .

[2]  Zhigang Zeng,et al.  Deformation Prediction of Landslide Based on Improved Back-propagation Neural Network , 2012, Cognitive Computation.

[3]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.

[4]  S. Roberts,et al.  Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks , 2001 .

[5]  A. Weigend,et al.  Estimating the mean and variance of the target probability distribution , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[6]  M. Matteucci,et al.  Artificial neural networks and cluster analysis in landslide susceptibility zonation , 2008 .

[7]  Thong Ngee Goh,et al.  Neural network modeling with confidence bounds: a case study on the solder paste deposition process , 2001 .

[8]  G. Reinsel,et al.  Prediction of multivariate time series by autoregressive model fitting , 1985 .

[9]  Saeid Nahavandi,et al.  Prediction Intervals to Account for Uncertainties in Travel Time Prediction , 2011, IEEE Transactions on Intelligent Transportation Systems.

[10]  Durga L. Shrestha,et al.  Machine learning approaches for estimation of prediction interval for the model output , 2006, Neural Networks.

[11]  Chris Chatfield,et al.  Calculating Interval Forecasts , 1993 .

[12]  S. Nahavandi,et al.  Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts , 2013, IEEE Transactions on Sustainable Energy.

[13]  George Chryssolouris,et al.  Confidence interval prediction for neural network models , 1996, IEEE Trans. Neural Networks.

[14]  Hongjie Chen,et al.  Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches , 2014, Landslides.

[15]  C. L. Philip Chen,et al.  A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[16]  J. T. Hwang,et al.  Prediction Intervals for Artificial Neural Networks , 1997 .

[17]  Sijing Wang,et al.  A nonlinear dynamical model of landslide evolution , 2002 .

[18]  Henry Leung,et al.  Prediction Intervals for a Noisy Nonlinear Time Series Based on a Bootstrapping Reservoir Computing Network Ensemble , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Zhigang Zeng,et al.  Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis , 2013, Neural Computing and Applications.

[20]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[21]  C. L. Philip Chen,et al.  A rapid supervised learning neural network for function interpolation and approximation , 1996, IEEE Trans. Neural Networks.

[22]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Nozer D. Singpurwalla,et al.  Choosing a Coverage Probability for Prediction Intervals , 2008 .

[24]  R. D. Veaux,et al.  Prediction intervals for neural networks via nonlinear regression , 1998 .

[25]  Z. Zeng,et al.  Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level , 2014, Stochastic Environmental Research and Risk Assessment.

[26]  Sijing Wang,et al.  The predictable time scale of landslides , 2001 .

[27]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[28]  Igor Kononenko,et al.  Prediction intervals in supervised learning for model evaluation and discrimination , 2014, Applied Intelligence.

[29]  Abbas Khosravi,et al.  Particle swarm optimization for construction of neural network-based prediction intervals , 2014, Neurocomputing.

[30]  J. Shao,et al.  Gauss Process Based Approach for Application on Landslide Displacement Analysis and Prediction , 2012 .

[31]  Xiuzhen Li,et al.  Landslide displacement prediction based on combining method with optimal weight , 2012, Natural Hazards.

[32]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[33]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[34]  Amir F. Atiya,et al.  Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances , 2011, IEEE Transactions on Neural Networks.

[35]  Olvi L. Mangasarian,et al.  Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization , 2006, J. Mach. Learn. Res..

[36]  Amir F. Atiya,et al.  Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.

[37]  Nigel Meade,et al.  Prediction intervals for growth curve forecasts , 1995 .

[38]  S. Balasundaram,et al.  1-Norm extreme learning machine for regression and multiclass classification using Newton method , 2014, Neurocomputing.

[39]  Rob J Hyndman,et al.  Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .

[40]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[41]  Suzanne Lacasse,et al.  Displacement prediction in colluvial landslides, Three Gorges Reservoir, China , 2013, Landslides.