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Machine Learning-Based Earthquake Damage Assessment for Buildings: A Study with CatBoost, AdaBoost, XGBoost and LightGBM
Authors
Rahi Bhand,Tanmay Bora,Viraj Zuluk,Rushikesh Ghodke,Manisha Mali
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Keywords
Earthquake Damage Prediction, Building Damage Assessment, Ensemble Learning, CatBoost, XGBoost, AdaBoost, LightGBM, Machine Learning, Stacking Ensemble, Structural Attributes, Gorkha Earthquake Dataset
Abstract
In the face of unpredictable natural calamities, the capacity to quantify structural damage with precision could redefine the boundaries of post-disaster recovery and resource allocation. Automating the classification of building damage can expedite the process of distributing aid and reduce reliance on manual inspection. Accurate damage assessment is critical not only for post-earthquake recovery but also for taking pre-emptive measures to strengthen vulnerable structures. In this paper, we explore machine learning techniques such as AdaBoost, CatBoost, and a stacking ensemble to classify earthquake-induced building damage using data from the Gorkha earthquake. The methodology incorporates steps such as feature encoding, model training, and ensemble learning to enhance prediction accuracy. Key structural features--including building age, number of floors, foundation type, area percentage, and secondary usage--are integrated into the models to assess the predicted damage severity.
IJCRT's Publication Details
Unique Identification Number - IJCRT2411670
Paper ID - 272916
Author type - Indian Author
Page Number(s) - f953-f960
Pubished in - Volume 12 | Issue 11 | November 2024
DOI (Digital Object Identifier) -
No Of Downloads - 127
Author Country - India, 411048, Pune, Pune, 411048, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Rahi Bhand,Tanmay Bora,Viraj Zuluk,Rushikesh Ghodke,Manisha Mali, "Machine Learning-Based Earthquake Damage Assessment for Buildings: A Study with CatBoost, AdaBoost, XGBoost and LightGBM", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 11, pp.f953-f960, November 2024, Available at :http://www.ijcrt.org/papers/IJCRT2411670.pdf
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