Risk Prediction of Earthquakes using Machine Learning
B. Arunadevi, M. Madiajagan, Mohammed Ihtesham Hussain, R. Lakshmi, M.M. Ramakrishna, Kakali Sengupta Das
Abstract
As the population of earthquake-prone areas continues to grow, the number of people killed and injured by earthquakes is also increasing. There are an increasing number of populated places that are at risk from earthquakes, but there is only a limited number of earthquake engineering professionals to deal with the problem. As a result of this, automatic intelligence-monitored warning and mitigation systems are required. According to the findings, in this paper, an artificial intelligence model based on machine learning is being built to enable an autonomous approach for analysing and identifying risk potential, then alerting stakeholders and initiating risk reduction steps. It is possible to monitor slope movement and deformations remotely and autonomously in a variety of ways, including through the use of automated seismograph connections and collaboration with local, state, and national disaster relief organisations. Damage models typical of India current urban infrastructure can be used in modelling and simulation scenarios, and nonlinear seismic analysis scenarios can be used for these models. People who work with machine learning in earth quake prediction applications can use data mining techniques like decision trees and geographic information systems to make their applications smarter by making them smarter.