VOLUME 70 : 2022

Detection of Earthquake Damages from Satellite Images using Gradient Boosting Algorithm with Decision Trees as Base Estimator

PAGE 13-28

Donata D. Acula

The Philippines experiences an average of twenty (20) earthquakes every day and 100 to 150 felts every year. With the aim to help the government in mitigation of the potential impact of the earthquake in the country, this research explored the detection of earthquake damages from the satellite images. Since Gradient Boosting Algorithm is considered one of the efficient and powerful predictive models, this method was employed in this research to classify if the satellite images before and after the earthquake brought damages in the infrastructure of affected areas. The satellite images used in the research were downloaded from Landsat 8 via Google Earth pro, which focused on a Magnitude 7.6 earthquake in Eastern Samar on August 31, 2012. The research used image augmentation such as rotation, shearing and flipping before the image extraction. The extracted features were used as classifiers of the models and were separated into 80:20, 70:30 and 60:20 ratios for training and testing sets respectively. The detection of damages was evaluated five (5) times using different n-estimators or numbers of trees {10, 20, 30, 40, 50}. The experiment concluded that Gradient Boosting Algorithm is an efficient model for classification and detection of earthquake damages using satellite image data, with an optimal detection accuracy of 85.71% and 100% without and with image augmentation.

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