What size net gives valid generalization?
Neural Computation
Dimensionality analysis of time-series data: nonlinear methods
Computers & Geosciences
Nonlinear time series analysis
Nonlinear time series analysis
Neural networks to predict earthquakes in Chile
Applied Soft Computing
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This study utilizes two non-linear approaches to characterize model behavior of earthquake dynamics in the crucial tectonic regions of Northeast India (NEI). In particular, we have applied a (i) non-linear forecasting technique to assess the dimensionality of the earthquake-generating mechanism using the monthly frequency earthquake time series (magnitude =4) obtained from NOAA and USGS catalogues for the period 1960-2003 and (ii) artificial neural network (ANN) methods-based on the back-propagation algorithm (BPA) to construct the neural network model of the same data set for comparing the two. We have constructed a multilayered feed forward ANN model with an optimum input set configuration specially designed to take advantage of more completely on the intrinsic relationships among the input and retrieved variables and arrive at the feasible model for earthquake prediction. The comparative analyses show that the results obtained by the two methods are stable and in good agreement and signify that the optimal embedding dimension obtained from the non-linear forecasting analysis compares well with the optimal number of inputs used for the neural networks. The constructed model suggests that the earthquake dynamics in the NEI region can be characterized by a high-dimensional chaotic plane. Evidence of high-dimensional chaos appears to be associated with ''stochastic seasonal'' bias in these regions and would provide some useful constraints for testing the model and criteria to assess earthquake hazards on a more rigorous and quantitative basis.