Qi et al 2021 AI based susceptibility analysis of shallow landslides Remote Sensing-13-01819-1.pdf (8.23 MB)
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AI-based susceptibility analysis of shallow landslides induced by heavy rainfall in Tianshui, China

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posted on 14.05.2021, 10:15 authored by Tianjun Qi, Yan Zhao, Xingmin Meng, Guan Chen, Tom DijkstraTom Dijkstra
Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

Funding

National Key R&D Program of China (Grant No. 2018YFC1504704)

Science and Technology Major Project of Gansu Province (Grant No. 19ZD2FA002)

Program for International S&T Cooperation Projects of Gansu Province (Grant No. 2018-0204-GJC0043)

Fundamental Research Funds for the Central Universities (lzujbky-2018-46)

Key Research and Development Program of Gansu Province (Grant No. 18YF1WA114)

History

School

  • Architecture, Building and Civil Engineering

Published in

Remote Sensing

Volume

13

Issue

9

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

02/05/2021

Publication date

2021-05-07

Copyright date

2021

eISSN

2072-4292

Language

en

Depositor

Dr Tom Dijkstra. Deposit date: 14 May 2021

Article number

1819