Temporal detection of sharp landslide deformation with ensemble-based LSTM-RNNs and Hurst exponent
The sharp slope deformation which often contains seasonal patterns is the major source of the landslide hazard with respect to the local community, which it is a serious geological environment problem. In this paper, a long short-term memory-based deep learning framework has been proposed to model t...
Guardado en:
Autores principales: | Huajin Li, Qiang Xu, Yusen He, Xuanmei Fan, He Yang, Songlin Li |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Taylor & Francis Group
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/c578abac1dae4e7f94f5086c17b5ccc0 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Relationship between Continuum of Hurst Exponents of Noise-like Time Series and the Cantor Set
por: Maria C. Mariani, et al.
Publicado: (2021) -
A Climate-Mathematical Clustering of Rainfall Stations in the Río Bravo-San Juan Basin (Mexico) by Using the Higuchi Fractal Dimension and the Hurst Exponent
por: Francisco Gerardo Benavides-Bravo, et al.
Publicado: (2021) -
Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment
por: Patricia Arrogante-Funes, et al.
Publicado: (2021) -
Triggering mechanism and possible evolution process of the ancient Qingshi landslide in the Three Gorges Reservoir
por: Luqi Wang, et al.
Publicado: (2021) -
Small-Angle Scattering from Fractional Brownian Surfaces
por: Eugen Mircea Anitas
Publicado: (2021)