Volume 6, Issue 3, September 2020, Page: 58-63
Earthquake Damage Prediction Using Random Forest and Gradient Boosting Classifier
Sourav Pandurang Adi, Electronics and Communication Engineering, RNS Institute of Technology, Bengaluru, Karnataka, India
Vivek Bettadapura Adishesha, Electronics and Communication Engineering, RNS Institute of Technology, Bengaluru, Karnataka, India
Keshav Vaidyanathan Bharadwaj, Electronics and Communication Engineering, RNS Institute of Technology, Bengaluru, Karnataka, India
Abhinav Narayan, Electronics and Communication Engineering, RNS Institute of Technology, Bengaluru, Karnataka, India
Received: Sep. 26, 2020;       Accepted: Oct. 13, 2020;       Published: Oct. 21, 2020
DOI: 10.11648/j.ajbes.20200603.14      View  74      Downloads  53
Earthquake is a major natural disaster that causes casualties in millions and leaving many more in trauma. Analyzing the consequences of such consequences gives one a better stand-in for potential catastrophe occurrences. It is important to establish a methodology that can assist in forecasting these earthquakes, as they can help prevent the severity of the damage. This paper discusses a machine learning model that can predict the damage grade severity caused by life-threatening earthquake that hit Nepal in the year 2015. The dataset is derived from the live competition hosted by Driven Data. The data was collected through the surveys conducted by the Kathmandu Living Labs and the Central Bureau of Statistics, which operates under the National Planning Commission Secretariat of Nepal. To accomplish the defined goal, we used the Random Forest Classifier and Gradient Boosting Classifier. The Random Forest Classifier algorithm demonstrated in this study was outperformed by the Gradient Boosting Classifier. With necessary parameter tuning using the Random Forest Classifier, the F1-Score achieved was 72.95%. The next technique was to perform winsorization on some attributes to handle outliers which improved the F1-score to 74.33% along with gradient boosting classifier. The last techniqueinvolved only hyper-parameter tuning with gradient boosting classifier achieved the best F1-Score of 74.42%.
Random Forest Classifier, Gradient Boosting Classifier, Winsorizing, Earthquake
To cite this article
Sourav Pandurang Adi, Vivek Bettadapura Adishesha, Keshav Vaidyanathan Bharadwaj, Abhinav Narayan, Earthquake Damage Prediction Using Random Forest and Gradient Boosting Classifier, American Journal of Biological and Environmental Statistics. Vol. 6, No. 3, 2020, pp. 58-63. doi: 10.11648/j.ajbes.20200603.14
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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