Prediction of landslide tsunami run-up on a plane beach through feature selected MLP-based model
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
We proposed new prediction models based on multilayer perceptron (MLP) which successfully predict the maximum run-up of landslide -generated tsunami waves and assess the role of parameters affecting it. The input is approximately 55,0 0 0 rows of data generated through an analytical solution employing slide's cross section, initial submergence, vertical thickness, horizontal length, beach slope angle and the maximum run-up itself, along with its occurrence time. The parameters are first ranked through a feature selection algorithm and six models are constructed for a 9,0 0 0 -row randomly sampled dataset. These MLP-based models led predictions with a minimum Mean Absolute Percentage Error of 1.1% and revealed that vertical slide thickness has the largest impact on the maximum tsunami run-up, whereas beach slope angle has minimal effect. Com parison with existing literature showed the reliability and applicability of the offered models. The methodology introduced here can be suggested as fast and flexible method for prediction of landslide -induced tsunami run-up. (c) 2022 Shanghai Jiaotong University. Published by Elsevier B.V. This is an open access article under the CC BY -NC -ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Açıklama
Anahtar Kelimeler
Landslide tsunami, Maximum run-up, Artificial neural networks, Feature selection
Kaynak
Journal of Ocean Engineering and Science
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
9
Sayı
3
