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Slide Model-Invariant Prediction of Landslide-Tsunamis Using Machine Learning

Author(s): David Jenkins; Valentin Heller; Archontis Giannakidis

Linked Author(s): Valentin Heller

Keywords: No Keywords

Abstract: Landslides impacting a body of water can generate large landslide-tsunamis. Therefore, producing reliable and fast methods of predicting such waves is vital. No single empirical equation exists for universally predicting the landslide-tsunami characteristics involving both granular and block slide models. To fill this gap, we created a machine learning model using the Gradient Boosting method to predict the relative maximum tsunami amplitude aM/h and height HM/h for both slide types, where h is the still water depth. Our model produced an R2 score of 0.919 for aM/h and 0.937 for HM/h. Our method has shown promise and opens us possibilities to employing machine learning in real-world landslide-tsunami predictions.

DOI:

Year: 2022

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