Explainable Machine learning on New Zealand strong motion for PGV and PGA

Somala, Surendra Nadh and Chanda, Sarit and Karthikeyan, Karthika and Mangalathu, Sujith (2021) Explainable Machine learning on New Zealand strong motion for PGV and PGA. Structures, 34. pp. 4977-4985. ISSN 2352-0124

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Estimating ground motion characteristics at various locations as a function of fault characteristics is useful for the proper damage assessment and risk mitigation strategies. This paper explores the application of machine learning approaches to predict peak ground acceleration (PGA) and peak ground velocity (PGV) using New Zealand's strong motion data. Five machine learning algorithms, namely linear regression, kNN, SVM, Random Forest, and XGBoost, are used in this study. Using the New Zealand flat-file database, the geometric mean of the peak ground motion parameters is used as predictor variables in training the machine learning algorithms. The performance of the chosen algorithms and how they work on PGV and PGA are discussed. The best prediction for PGA is obtained using random forest but for PGV XGboost worked best. The relative importance of various features in the flat file is also presented for the best-performing machine learning algorithm. Although the magnitude of an earthquake is found to be most influential for PGV, rupture distance showed the highest impact for PGA. Finally, the predictions are also explained using SHApley Additive exPlanations (SHAP) for the overall dataset as well as on a sample by sample basis, for a few samples. Pairwise dependency of some features with the highest feature importance is also presented using SHAP. © 2021 Institution of Structural Engineers

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IITH Creators:
IITH CreatorsORCiD
Somala, Surendra Nadhhttps://orcid.org/0000-0003-2663-3351
Item Type: Article
Additional Information: Funding from the Ministry of Earth Sciences (MoES), India, under the grant number MoES/P.O.(Seismo)/1(304)/2016 is greatly acknowledged.
Uncontrolled Keywords: Kaikoura; Machine learning; Pgv; Random forest; Svm; Xgboost
Subjects: Civil Engineering
Divisions: Department of Civil Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Aug 2022 06:19
Last Modified: 23 Aug 2022 06:19
URI: http://raiith.iith.ac.in/id/eprint/10260
Publisher URL: http://doi.org/10.1016/j.istruc.2021.10.085
OA policy: https://v2.sherpa.ac.uk/id/publication/33171
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