Publication Date: 2023/02/14
Abstract: The intensity of Seismic damage prediction is an important task that aims to predict seismic events in real time from historical data or seismic time series. Due to the increase in seismic data available over the past few decades, research in the field of seismic event detection has achieved considerable success using neural networks and other machine learning techniques. An earthquake is the negative impact. That it significantly harms a community. An earthquake causes loss of life. The system predicts the intensity of damage to be occurring in an earth quake with the help of previous text data. The system predicts magnitude and depth value. Using this value (Magnitude and depth) predict the intensity of damage. This work, proposes a random forest, KNearest Neighbor (KNN), and support vector machine (SVM) for earthquake damage prediction. Finding out the best model from various Machine Learning algorithms to build prediction models, evaluate the accuracy and performance of these models. Among these three methods Random forest regressor algorithm shows the most accurate result with 98% accuracy.
Keywords: KNN, SVM, Random Forest Regressor, Magnitude, and Depth.
DOI: https://doi.org/10.5281/zenodo.7638759
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23JAN1518.pdf
REFERENCES