Zobrazeno 1 - 10
of 248
pro vyhledávání: '"susceptibility prediction."'
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 10, Pp 4177-4191 (2024)
The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modelin
Externí odkaz:
https://doaj.org/article/6b749188568245bb9b21322fedcdcbf7
Publikováno v:
Frontiers in Earth Science, Vol 12 (2024)
Colluvial landslides widely developed in mountainous and hilly areas have the characteristics of mass occurrence and sudden occurrence. How to reveal the spatial distribution rules of potential landslides quickly and accurately is of great significan
Externí odkaz:
https://doaj.org/article/26be7798dd2f444cb1197bd3b6d4abfa
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 135, Iss , Pp 104217- (2024)
Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for lan
Externí odkaz:
https://doaj.org/article/0d1d2df4cdf44582aa5e3c01a1c5f475
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 15, Iss 1 (2024)
Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low e
Externí odkaz:
https://doaj.org/article/28f7311b8bdf433bbf9e4ba3094af7b5
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into positive samples (landslides) and unlabeled samples for tr
Externí odkaz:
https://doaj.org/article/d76e616e57534c499c865757a5906dee
Publikováno v:
International Journal of Coal Science & Technology, Vol 11, Iss 1, Pp 1-30 (2024)
Abstract This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its east
Externí odkaz:
https://doaj.org/article/aa6a049d3d50434d97f293119788173f
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 1, Pp 213-230 (2024)
In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which bri
Externí odkaz:
https://doaj.org/article/a7e82e9bbde24c55bafa7418a543b70b
Publikováno v:
Remote Sensing, Vol 16, Iss 19, p 3663 (2024)
The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples fr
Externí odkaz:
https://doaj.org/article/e42b87f180984c7ab618bde35ded4928
Autor:
Zhilu Chang, Filippo Catani, Faming Huang, Gengzhe Liu, Sansar Raj Meena, Jinsong Huang, Chuangbing Zhou
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 15, Iss 5, Pp 1127-1143 (2023)
To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale segmentation (MSS) method proposed by the authors promotes th
Externí odkaz:
https://doaj.org/article/16fd2d8ef3d44e188d632e082a728d79
Publikováno v:
Geocarto International, Vol 38, Iss 1 (2023)
Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an
Externí odkaz:
https://doaj.org/article/1a416076098d4e12874dc3ca255d89b8