Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Deliang Sun"'
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
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
Autor:
Youchen Zhu, Deliang Sun, Haijia Wen, Qiang Zhang, Qin Ji, Changming Li, Pinggen Zhou, Jianjun Zhao
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 15, Iss 1 (2024)
Crafting landslide susceptibility mapping is pivotal for the effective management of landslide risks. However, the influence of non-landslide sample selection on the modeling performance of landslide susceptibility assessment models remains a crucial
Externí odkaz:
https://doaj.org/article/eccbdd4649844af9b29dbaa0562b98ab
Publikováno v:
Water, Vol 16, Iss 17, p 2414 (2024)
The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sam
Externí odkaz:
https://doaj.org/article/0d799160bdcb4dc386370b2c671bbcd0
A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 14, Iss 1 (2023)
AbstractThe quality of samples is crucial in constructing a data-driven landslide susceptibility model. This article aims to construct a data-driven landslide susceptibility model that takes into account the selection of non-landslide samples. First,
Externí odkaz:
https://doaj.org/article/bb016fa7dd7a4069a7721e0d6e5572d4
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 14, Iss 1 (2023)
AbstractLandslides have differential characteristics in different regions. This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides’ differential characteristics in differ
Externí odkaz:
https://doaj.org/article/8a392b313e1942b2a1f97cfd2bd82b19
Publikováno v:
Land, Vol 12, Iss 5, p 1018 (2023)
(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi
Externí odkaz:
https://doaj.org/article/18cab6d401a04c259eee791ef0b9794d
Publikováno v:
Frontiers in Earth Science, Vol 9 (2021)
This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, wa
Externí odkaz:
https://doaj.org/article/6cf3e52def784346bc12a51f2308f1c8
Publikováno v:
Forests, Vol 13, Iss 10, p 1621 (2022)
The aim of the present study was to assess the suitability of mountainous areas for construction land on the basis of landslide susceptibility, to obtain the spatial distribution pattern of said suitability and to improve the existing theories and me
Externí odkaz:
https://doaj.org/article/7f3a36018f684774bdada8013b01c4a2
Publikováno v:
Forests, Vol 13, Iss 6, p 827 (2022)
Landslides are one of the most severe and common geological hazards in the world. The purpose of this research is to establish a coupled landslide warning model based on random forest susceptibility zoning and precipitation. The 1520 landslide events
Externí odkaz:
https://doaj.org/article/064e8033537c46989c65cd4acadf624e