Earthquake Time Prediction using CatBoost and SVR
Sahaya Sakila1, Sanyam Garg2, Tanay Yeole3, Hrithik Yadav4
1Sahaya Sakila*, Assistant Professor, Computer Science And Engineering Department, in SRM Institute of Science And Technology, Ramapuram, Chennai.
2Sanyam Garg, Pre-Final Year Student of B.Tech Computer Science And Engineering in SRM Institute of Science And Technology, Ramapuram, Chennai
3Tanay Yeole Pre-Final Year Student of B.Tech Computer Science And Engineering in SRM Institute of Science And Technology, Ramapuram, Chennai
4Hrithik Yadav, Pre-Final Year Student of B.Tech Computer Science And Engineering in SRM Institute of Science And Technology, Ramapuram, Chennai
Manuscript received on October 16, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 225-229 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3993119119/2019©BEIESP | DOI: 10.35940/ijitee.A3993.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Seismic tremors everywhere throughout the globe have been a noteworthy reason for decimation and death toll and property. The following context expects to recognize earthquakes at a beginning time utilizing AI. This will help individuals and salvage groups to make their errand simpler. The information in this manner comprises of these seismic acoustic signals and the time of failure. The model is then prepared utilizing the CatBoost model and the utilization of Support Vector Machines. This will help foresee the time at which a Seismic tremor may happen. CatBoost Regression Algorithm gives a Mean Absolute Error of about 1.860. The Cross Validation (CV) Score for the Support Vector Machine (SVM) approach is -2.1651. The datasets metrics are not reliable on any outer parameter in this manner the variety of exactness is constrained, and high accuracy is accomplished.
Keywords: Cat Boosting, Support Vector Regression, Acoustic Signals, Earthquake, Exploratory Data Analysis, Skew, RMS, Peak.
Scope of the Article: Earthquake Engineering