Large scale landslide susceptibility assessment based on machine-learning methods

Yu Wu; Jianrong Wu; Yi Li; Jibin Jiang; Shuyi Zheng

doi:10.1117/12.3026337

27 March 2024 Large scale landslide susceptibility assessment based on machine-learning methods

Yu Wu, Jianrong Wu, Yi Li, Jibin Jiang, Shuyi Zheng

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310519 (2024) https://doi.org/10.1117/12.3026337
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In large scale landslide susceptibility assessment, it is necessary to reflect the differences of landslide formation background, conditions and evaluation indexes. However, the susceptibility assessment methods for small and medium scale area are not applicable. Sinan County has complex geological conditions, larger population and intense human engineering activity, in the event of a landslide, would be extremely costly. Thus, the site is a typical area for large scale and high precision susceptibility assessment. In order to explore locally adapted landslide susceptibility assessment method, 10 evaluation indicators, including altitude, slope, aspect, slope aspect, lithology, distance to fault, TWI, NDVI, distance to road and land types, are selected to evaluate landslide susceptibility, through Logistic Regression model (LR), Decision Tree model (DT) and Random Forest model (RF). Experimental results of model evaluation using receiver operating characteristics (ROC), area under the curve (AUC) and accuracy (ACC) showed that the RF (ROC=0.959, ACC=0.897) were more accurate than DT (ROC=0.925, ACC=0.884) and LR (ROC=0.786, ACC=0.703). High and very high landslide-prone areas are mainly concentrated in the steep slope of the valley bank, the geological environment is fragile, and the human engineering activities are strong. The results from this study demonstrates that the RF can identify landslides well in large-scale landslide susceptibility assessment, and provides scientific basis for disaster prevention and mitigation in large-scale landslide susceptibility assessment of mountain towns.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Wu, Jianrong Wu, Yi Li, Jibin Jiang, and Shuyi Zheng "Large scale landslide susceptibility assessment based on machine-learning methods", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310519 (27 March 2024); https://doi.org/10.1117/12.3026337

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Matrices

Lawrencium

Random forests

Roads

Engineering

Statistical modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years