Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Deliang Sun"'
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:
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
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:
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:
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 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
Publikováno v:
Remote Sensing, Vol 13, Iss 13, p 2625 (2021)
This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved develo
Externí odkaz:
https://doaj.org/article/efe2c0ac5fe24bdeb554e55d4de7c40a
Autor:
Deliang Sun, Mahshid Lonbani, Behnam Askarian, Danial Jahed Armaghani, Reza Tarinejad, Binh Thai Pham, Van Van Huynh
Publikováno v:
Applied Sciences, Vol 10, Iss 5, p 1691 (2020)
Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well
Externí odkaz:
https://doaj.org/article/dcafb748dd194642bee9f577a5469453