Deformation prediction of landslide based on functional network

This paper proposes functional networks as novel intelligence paradigm scheme for landslide displacement prediction. They evaluate unknown neuron functions from given functional families during the training process. General functional networks with two variables training data set (GFN), separable functional networks (SFN) and associativity functional networks (AFN) are applied to forecast a real-world example. In addition, we compare them with back-propagation neural network (BPNN) in terms of the same measurements. The results reveal that the landslide displacement prediction using functional networks is reasonable and effective, and GFN are consistently better than the other two types of functional networks and BPNN.

[1]  N. Ebecken,et al.  A Data Based Model to Predict Landslide Induced by Rainfall in Rio de Janeiro City , 2012, Geotechnical and Geological Engineering.

[2]  Oscar Fontenla-Romero,et al.  Functional Networks , 2009, Encyclopedia of Artificial Intelligence.

[3]  Leonardo Cascini,et al.  Susceptibility analysis of shallow landslides source areas using physically based models , 2010 .

[4]  Enrique F. Castillo,et al.  Semi-parametric nonlinear regression and transformation using functional networks , 2008, Comput. Stat. Data Anal..

[5]  Bernardino Arcay Varela,et al.  Optimisation of fishing predictions by means of artificial neural networks, anfis, functional networks and remote sensing images , 2005, Expert Syst. Appl..

[6]  E. Castillo,et al.  Functional Networks: A New Network‐Based Methodology , 2000 .

[7]  Harris Drucker,et al.  Improving generalization performance using double backpropagation , 1992, IEEE Trans. Neural Networks.

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

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

[10]  Jun Wu,et al.  Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model , 2012, Neurocomputing.

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

[12]  Chunshien Li,et al.  A novel self-organizing complex neuro-fuzzy approach to the problem of time series forecasting , 2013, Neurocomputing.

[13]  PradhanBiswajeet,et al.  Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping , 2011 .

[14]  D. P. Kanungo,et al.  Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides , 2011 .

[15]  Michael Bruen,et al.  Functional networks in real-time flood forecasting—a novel application , 2005 .

[16]  José Manuel Gutiérrez,et al.  SOME LEARNING METHODS IN FUNCTIONAL NETWORKS , 2000 .

[17]  Carlos Dafonte,et al.  A comparison between functional networks and artificial neural networks for the prediction of fishing catches , 2004, Neural Computing & Applications.

[18]  Ali S. Hadi,et al.  Iterative Least Squares Functional , 2007 .

[19]  Shih-Hung Yang,et al.  An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications , 2012, Neurocomputing.

[20]  Amro El-Jaroudi,et al.  A new error criterion for posterior probability estimation with neural nets , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[21]  Mengjie Zhang,et al.  Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction , 2012, Neurocomputing.

[22]  Emad A. El-Sebakhy,et al.  Software reliability identification using functional networks: A comparative study , 2009, Expert Syst. Appl..

[23]  Zhigang Zeng,et al.  Displacement Prediction Model of Landslide Based on Ensemble of Extreme Learning Machine , 2012, ICONIP.

[24]  Emad A. El-Sebakhy,et al.  Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir , 2012, Expert Syst. Appl..

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

[26]  Zhigang Zeng,et al.  Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine , 2013, Natural Hazards.

[27]  Enrique F. Castillo,et al.  Some Applications of Functional Networks in Statistics and Engineering , 2001, Technometrics.

[28]  Enrique F. Castillo,et al.  A Minimax Method for Learning Functional Networks , 2000, Neural Processing Letters.

[29]  Enrique F. Castillo,et al.  A general framework for functional networks , 2000, Networks.

[30]  Biswajeet Pradhan,et al.  Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia , 2011, Expert Syst. Appl..

[31]  Enrique Castillo,et al.  Nonlinear time series modeling and prediction using functional networks. Extracting information masked by chaos , 1998 .

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

[33]  Emad A. El-Sebakhy.  Functional networks as a novel data mining paradigm in forecasting software development efforts , 2011, Expert Syst. Appl..