Introduction to Rusho’s Transform Lakshmann and Smith Model: A Machine Learning Approach to Earthquake Detection

Introduction to Rusho’s Transform Lakshmann and Smith Model: A Machine Learning Approach to Earthquake Detection

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Author(s): Maher Ali Rusho

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DOI: 10.18483/ijSci.2646 77 161 1-5 Volume 12 - Jan 2023


Machine learning now become one of the demandable field in engineering and life sciences , IT is now the cutting edge of technological development , The threat of climate change makes it crucial to improve our understanding of the climate system . This paper has been inspired by the book : Machine Learning and Data Mining Approaches to climate science . From this book I am inspired to find a new branch of machine learning for the earthquake detection and intro climate +physics or Climato -physics Modelling . Using Artificial intelligence is one of the remarkable steps to find the optimal solution of Earthquake detection . Approximately 20,000 people are killed every year by earthquakes. There are over 500 active faults in California and most of its residents live within 30 miles of an active fault. So if we can predict the earth-quake before it occurred , we can make active step and save millions of life . It is similar to weather detection and thunderstorms detection , same algorithm will be implemented in thunderstorm and weather detection , but we will use both as well as machine learning model and artificial intelligence model ,Rats, weasels, snakes, and centipedes reportedly left their homes and headed for safety several days before a destructive earthquake. Anecdotal evidence abounds of animals, fish, birds, reptiles, and insects exhibiting strange behavior anywhere from weeks to seconds before an earthquake. So we will first understand why and how earthquake happen and biological mechanism of understanding fish , birds , reptiles and insects hearing . Then we will use the datasets of global earthquake pattern . And finally we will put all in one and relate the algorithm with storm-detection.

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

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