A comparative analysis of ensemble learning algorithms with hyperparameter optimization for soil liquefaction prediction

Accurate prediction of soil liquefaction potential is crucial for evaluating the stability of structures in earthquake regions. This study focuses on predicting soil liquefaction using a dataset that included historical liquefaction cases from the 1999 Turkey and Taiwan earthquakes. The dataset was...

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Veröffentlicht in:	Environmental earth sciences 2024-05, Vol.83 (9), p.289-289, Article 289
Hauptverfasser:	Demir, Alparslan Serhat, Kurnaz, Talas Fikret, Kökçam, Abdullah Hulusi, Erden, Caner, Dağdeviren, Uğur
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Biogeosciences Coarse-grained soils Comparative analysis data collection Datasets Earth and Environmental Science Earth Sciences Earthquake prediction Earthquakes Ensemble learning Environmental Science and Engineering Fine-grained soils Genetic algorithms Geochemistry Geology Geotechnical engineering Hydrology/Water Resources Learning algorithms Liquefaction Machine learning Optimization Original Article Performance assessment Performance evaluation prediction Predictions Seismic activity Seismic stability Soil soil liquefaction Soils Taiwan Terrestrial Pollution Training
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