An Adaptive Gaussian Kernel for Support Vector Machine

Elen, Abdullah; Baş, Selçuk; Közkurt, Cemil

doi:10.1007/s13369-022-06654-3

Cited by 24 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8). A 7 × 7 Gaussian similarity matrix, A, was formulated to express the spatial relationships between the monitoring points, where the matrix values were derived from a Gaussian similarity function (Elen et al 2022) (as delineated in Eq. ( 18).…”

Section: Comparative Experiments On Prediction Accuracy and Efficiencymentioning

confidence: 99%

Spatiotemporal prediction of landslide deformation based on temporal graph convolutional networks

Ma,

Yao,

Deng

et al. 2024

Preprint

View full text Add to dashboard Cite

The displacement-time curves of landslides accurately reflect their movement status. Precise prediction of landslide deformation is the key to successful early warning. Machine-learning techniques have been used to predict the deformation of individual landslide monitoring points with desirable results. However, previous studies have not considered the spatial correlation between the monitoring points arranged in the horizontal and vertical profiles. Based on the deep learning model of a temporal graph convolutional network (T-GCN), a feasible solution was provided to accurately predict the overall deformation of landslides. To capture spatial and temporal correlations simultaneously, this study proposed a T-GCN spatiotemporal prediction method that considers the temporal correlation effects of the external factors inducing landslide deformation and generates comprehensive prediction results. The model combines the characteristics of a graph convolutional network (GCN) and gated recurrent unit (GRU). The GCN was used to determine the spatial correlations between landslide monitoring points, whereas the GRU was used to capture dynamic changes in displacement over time. The T-GCN model was then applied to predict the spatiotemporal deformation of the Dawuchang landslide in the Three Gorges Reservoir area. The experimental results demonstrate that the T-GCN model provides a novel solution for spatiotemporal deformation prediction of landslides and can effectively predict the overall displacement of landslides.

show abstract

Section: Comparative Experiments On Prediction Accuracy and Efficiencymentioning

confidence: 99%

Spatiotemporal prediction of landslide deformation based on temporal graph convolutional networks

Ma,

Yao,

Deng

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…“The property α of a trained SVM model stores the difference between two Lagrange multipliers of support vectors, (α n – α n *). The properties Support Vectors and Bias store x n and b, respectively” [ [45] , [46] , [47] ].…”

Section: Experimentation Detailsmentioning

confidence: 99%