Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor

Dr.Kapil Goswami; Gupta Dhawal

Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor

Dr.Kapil Goswami * §

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Gupta Dhawal

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§ Indian Institute of Technology

Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor

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Abstract

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.

References

18 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr.Kapil Goswami, Gupta Dhawal. 2013. "Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 13 (GJCST Volume 13 Issue D2).

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GJCST Volume 13 Issue D2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

anomalies earthquake precursor Neural networks radon

Classification

GJCST-D Classification F.1.1

F.2.1

Submission ReceivedApril 8, 2013
Peer Review Double Blind
Handling Editor
Accepted May 3, 2013
Published May 15, 2013

Version of record

v1.2

Issue date

May 19, 2013

Language

English

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Article Matrices

Total Score: 142

Country: Unknown

Subject: Global Journal of Computer Science and Technology

Authors: Gupta Dhawal, Shahani D. T (PhD/Dr. count: 0)

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Publish Date: 2013 05, Wed

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Total Views: 3.3K

Total Downloads: 268

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