Zobrazeno 1 - 10
of 7 311
pro vyhledávání: '"Landslide susceptibility"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-24 (2024)
Abstract In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimiza
Externí odkaz:
https://doaj.org/article/b090d347f4884c15945fda07c3dfb164
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:
Egyptian Journal of Remote Sensing and Space Sciences, Vol 27, Iss 3, Pp 508-523 (2024)
For landslide prevention and control, it is essential to establish a landslide susceptibility prediction framework that can explain the model’s decision-making process. Wushan County, Chongqing was selected as the study area, and seventeen landslid
Externí odkaz:
https://doaj.org/article/6fe082c8f95b4516aa63c9a572921712
Autor:
Qing ZHANG, Yi HE, Xueye CHEN, Binghai GAO, Lifeng ZHANG, Zhanao ZHAO, Jiangang LU, Yalei ZHANG
Publikováno v:
Zhongguo dizhi zaihai yu fangzhi xuebao, Vol 35, Iss 4, Pp 146-162 (2024)
Convolutional neural network (CNN) models are widely used in landslide susceptibility assessment due to their powerful feature extraction capabilities, and traditional CNN is no longer able to meet the requirements. Therefore, this paper proposes a m
Externí odkaz:
https://doaj.org/article/9cefe83e81254371a909a783eb93677f
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 8, Pp 3221-3232 (2024)
Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping (LSM) studies. However, these algorithms possess distinct computational strategies and hyperparameters, making it challenging to propose an ideal LSM
Externí odkaz:
https://doaj.org/article/6029f72b38414ba7887c01b27b78457c
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 8, Pp 3192-3205 (2024)
Landslide susceptibility mapping is an integral part of geological hazard analysis. Recently, the emphasis of many studies has been on data-driven models, notably those derived from machine learning, owing to their aptitude for tackling complex non-l
Externí odkaz:
https://doaj.org/article/caec0fabc9574ee5bc7107821e90be77