Application of nature-inspired computing and implementation of algorithm for earthquake detection

Authors

  • PRIYANKA KUMARI Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India
  • SUNIL KUMAR Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India
  • RAM KUMAR GIRI India Meteorological Department, MoES, Lodi Road, New Delhi – 110 003, India
  • LAXMI PATHAK India Meteorological Department, MoES, Lodi Road, New Delhi – 110 003, India

DOI:

https://doi.org/10.54302/mausam.v75i2.5941

Keywords:

SEISAN, chaotic, unpredictable, damage, loss and property

Abstract

Improve learning techniques and to prepare reference entropy which measures from the field of information theory, building upon entropy  generally calculating the difference between two probability distributions. Cross-entropy can be used as a loss function when optimizing classification models like logistic regression and artificial neural networks. The performance of the proposed neural network with respect to cross entropy is presented in this research.  The performance can be improved by including more data and optimization. The proposed research work will be used for time series data of events detection and prediction such as seismic event’s (Earthquake).The point of the present work is to tune the suitable algorithms for meaningful detection of the disastrous earthquake events and to generate the proper timely warning to the public.

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Published

01-04-2024

How to Cite

[1]
P. KUMARI, S. KUMAR, R. K. GIRI, and L. PATHAK, “Application of nature-inspired computing and implementation of algorithm for earthquake detection”, MAUSAM, vol. 75, no. 2, pp. 507–514, Apr. 2024.

Issue

Section

Research Papers

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