An interpretable evolutionary extreme gradient boosting algorithm for rock slope stability assessment

Globally, slope failures cause severe disasters and substantial financial losses annually. Recent advancements in machine learning (ML) algorithms and dataset collection have created alternate solutions for complex slope stability problems. However, rock slope stability prediction remains a challeng...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Multimedia tools and applications Ročník 83; číslo 16; s. 46851 - 46874
Hlavní autoři:	Fatty, Abdoulie, Li, An-Jui, Qian, Zhi-Guang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.05.2024 Springer Nature B.V
Témata:	Algorithms Artificial intelligence Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Evolutionary algorithms Genetic algorithms Machine learning Mathematical models Multimedia Information Systems Parameters Prediction models Slope stability Special Purpose and Application-Based Systems Machine learning algorithms Extreme gradient boosting Model interpretability Genetic algorithm Slope stability prediction
ISSN:	1573-7721, 1380-7501, 1573-7721
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!