Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters

Chong Niu, Wenping Yin (), Wei Xue, Yujing Sui, Xingqing Xun, Xiran Zhou, Sheng Zhang and Yong Xue ()
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Chong Niu: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Wenping Yin: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Wei Xue: Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China
Yujing Sui: Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China
Xingqing Xun: Shandong GEO-Surveying and Mapping Institute, Jinan 250002, China
Xiran Zhou: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Sheng Zhang: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yong Xue: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Land, 2023, vol. 12, issue 1, 1-15

Abstract: Identification of potential landslide hazards is of great significance for disaster prevention and control. CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks) and many other deep learning methods have been used to identify landslide hazards. However, most samples are made with a fixed window size, which affects recognition accuracy to some extent. This paper presents a multi-window hidden danger identification CNN method according to the scale of the landslide in the experimental area. Firstly, the hidden danger area is preliminarily screened by InSAR deformation processing technology. Secondly, based on topography, geology, hydrology and human activities, a total of 15 disaster-prone factors are used to create factor datasets for in-depth learning. According to the general scale of the landslide, models with four window sizes of 48 × 48, 32 × 32, 16 × 16 and 8 × 8 are trained, respectively, and several window models with better recognition effect and suitable for the scale of landslide in the experimental area are selected for the accurate identification of landslide hazards. The results show that, among the four windows, 16 × 16 and 8 × 8 windows have the best model recognition effect. Then, according to the scale of the landslide, these optimal windows are pertinently selected, and the precision, recall rate and F-measure of the multi-window deep learning model are improved (82.86%, 78.75%, 80.75%). The research results prove that the multi-window identification method of landslide hazards combining InSAR technology and factors predisposing to disasters is effective, which can play an important role in regional disaster identification and enhance the scientific and technological support ability of geological disaster prevention and mitigation.

Keywords: landslide hazard identification; deep learning; multi-window; InSAR; factors predisposing to disasters (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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