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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Oct 2020

05 Oct 2020

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This preprint is currently under review for the journal NHESS.

Landslide risk zoning in Ruijin, Jiangxi, China

Xiaoting Zhou1, Weicheng Wu1, Ziyu Lin1, Guiliang Zhang2, Renxiang Chen2, Yong Song2, Zhiling Wang2, Tao Lang2, Yaozu Qin1, Penghui Ou1, Wenchao Huangfu1, Yang Zhang1, Lifeng Xie1, Xiaolan Huang1, Xiao Fu1, Jie Li1, Jingheng Jiang1, Ming Zhang1, Yixuan Liu1, Shanling Peng1, Chongjian Shao1, Yonghui Bai1, Xiaofeng Zhang3, Xiangtong Liu4, and Wenheng Liu1 Xiaoting Zhou et al.
  • 1Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang, 330013 Jiangxi, China
  • 2264 Geological Team of Jiangxi Nuclear Industry, Ganzhou, Jiangxi, China
  • 3School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, 330013 Jiangxi, China
  • 4Faculty of Geomatics, East China University of Technology, Nanchang, 330013 Jiangxi, China

Abstract. Landslides are one of the major geohazards threatening human society. This study was aimed at conducting such a hazard risk prediction and zoning based on an efficient machine learning approach, Random Forest (RF), for Ruijin, Jiangxi, China. Multiple geospatial and geo-environmental data such as land cover, NDVI, landform, rainfall, stratigraphic lithology, proximity to faults, to roads and to rivers, depth of the weathered crust, etc., were utilized in this research. After pre-processing, including digitization, linear feature buffering and value assignment, 19 hazard-causative factors were eventually produced and converted into raster to constitute a 19-band geo-environmental dataset. 155 observed landslides that had truly taken places in the past 10 years were utilized to establish a vector layer. 70 % of the disaster sites (points) were randomly selected to compose a training set (TS) and the remained 30 % to form a validation set (VS). A number of non-risk samples were identified in low slope (< 1–3°) areas and also added to the TS and VS in the similar percentage. Then, RF-based classification algorithm was employed to model the probability of landslide occurrence using the above 19-band dataset as predictive variables and TS for training. After performance evaluation, the RF-based model was applied back to the integrated dataset to calculate the probability of the hazard occurrence in the whole study area. The predicted map was evaluated versus both TS and VS and found of high reliability in which the Overall Accuracy (OA) and Kappa Coefficient (KC) are 91.49 % and 0.8299 respectively. In terms of the risk probability, the predicted map was further zoned into different risk grades to constitute landslide risk map. Modeling results also revealed the order of importance of the 19 causative factors, and the most important ones are the proximity to roads, slope, May–July rainfall, NDVI and elevation. We hence conclude that the RF algorithm is able to achieve the risk prediction with high accuracy and reliability, and this study may provide an operational methodology for geohazard risk mapping and assessment. The results of this study can serve as reference for the local authorities in prevention and early warning of landslide hazard.

Xiaoting Zhou et al.

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Xiaoting Zhou et al.

Xiaoting Zhou et al.


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Latest update: 26 Oct 2020
Publications Copernicus
Short summary
The paper is focused on landslides risk mapping and assessment using machine learning, notably, Random Forests algorithm, taking 19 geo-environmental factors into account. A number of innovative procedures were introduced in the research and reliable results of high accuracy obtained. We believe that the risk mapping approach developed in this paper is relevant and extendable to elsewhere, and the results can serve as reference for disaster prevention and early warning for the local government.
The paper is focused on landslides risk mapping and assessment using machine learning, notably,...