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2024 | OriginalPaper | Buchkapitel

16. The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction

verfasst von : Nerusupalli Dinesh Kumar Reddy, Ashok Kumar Gupta, Anil Kumar Sahu

Erschienen in: Geomorphic Risk Reduction Using Geospatial Methods and Tools

Verlag: Springer Nature Singapore

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Abstract

In this study, post-liquefaction Standard penetration test (SPT) data from the Chi-Chi earthquake was collected and included into a Random Forest (RF) and Support Vector Machine (SVM) model by using a metaheuristic-based optimizer called the Cat Swarm Optimization (CSO). This was done in an effort to improve the accuracy of the projections across a number of different iterations. After that, the data were normalized, and a person correlation matrix and a chi-square test were used to establish the degree of link between the variables. After that, we chose the parameters for the RF and SVM models that would be used for both training and testing by using a random sampling technique. This allowed us to have complete control over the models. It was discovered that CSO not only improved the fitting of the model and the quality of the results, but it also speed up the procedure.

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Titel: The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction
verfasst von: Nerusupalli Dinesh Kumar Reddy
Ashok Kumar Gupta
Anil Kumar Sahu
Verlag: Springer Nature Singapore
Buch: Geomorphic Risk Reduction Using Geospatial Methods and Tools
Print ISBN: 978-981-9977-06-2

Electronic ISBN: 978-981-9977-07-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-7707-9_16

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