Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor
The investigation throughout the world in past two decades provides evidence which indicates that significance variation of radon and other soil gases may occur in association with major geophysical events such as earthquake events. The traditional statistical algorithm which included regression to remove the effect of the meteorological parameters from the as is measured radon along with additional variation that periodicity in seasonal variations is computed using Fast Fourier Transform has shown to improve reliability of prediction of earthquake The present paper deals with the use of neural network algorithms which can learn the behavior of radon with respect to known meteorological parameters. This method has potential of tracking “changing patterns” in dependence of radon on meteorological parameters and it may adapt to such changes on its own in due course of time. Another neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period is also presented.
