Abstract
Due to the huge losses caused by landslides, landslide deformation monitoring has become essential. The change of the tilting angle of the slope surface can reflect the instability process of the landslide. However, there are few studies on the relationship between the instability process of landslides and the tilting angle of the slope surface on the time scale. Therefore, there are even fewer cases in using the inclination angle of the landslide as an early warning method in practice. On August 24, 2018, a wireless tilt sensor network was deployed in the Heifangtai area of Gansu Province in China, to monitor the inclination state of loess landslides to study the relationship between the tilting angle of the slope surface and the landslide instability process. At 4 o’clock on October 5, 2019, the wireless tilt sensor network mentioned above detected a new loess fall landslide. Through the analysis of the monitoring data from the tilting sensor nodes named T3 and T4, we found that the tilting angle of the landslide surface in the pre-sliding stage has significant change compared with the stable stage, which directly verifies that the inclination data can be used for landslide warning. At the same time, we inputted the monitored inclination data into the inclination warning model proposed by **e et al. (Landslides 17:301–312, 2020) for verification and found that the trend of inclination changes matches the model well. However, the forecast time error is large. Therefore, this paper optimizes the inclination early warning model proposed by **e. Using the optimized early warning model, the predicted landslide occurrence time based on the data of the T3 node was 0.517 days, which was about 8 h later than the actual landslide that occurred on October 4, 2019, which is an improvement of forecasting. This case serves as an on-site direct evidence to further verify the feasibility of using the landslide body inclination state as a landslide monitoring and early warning parameter which can be promoted and used in practice.
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Acknowledgements
The authors would like to thank graduate students Tianxiang Zhuo, Dingkang Zou, and Chaoyu Wei for their help during the deployment of the system. We also want to give great thanks to Wisen Innovation Company, Wuxi, China, for offering the Wisen wireless sensor system.
Funding
This work was sponsored by the National Natural Science Foundation of China (41521002), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2019Z012), and the Sichuan Science and Technology Program (2021YFS0324). It was also partially supported by the China Scholarship Council (201808510005).
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Wang, H., Zhong, P., **u, D. et al. Monitoring tilting angle of the slope surface to predict loess fall landslide: an on-site evidence from Heifangtai loess fall landslide in Gansu Province, China. Landslides 19, 719–729 (2022). https://doi.org/10.1007/s10346-021-01727-0
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DOI: https://doi.org/10.1007/s10346-021-01727-0