Abstract
The landslide geological disasters have many hazard factors, and the data indicators for monitoring geological disasters are not the same. Through the use of various intelligent sensors, multi-angle and multi-source monitoring of landslide geological disasters can be carried out. The deep displacement is an important parameter to reflect the deformation depth and range of the landslide body. The surface displacement, groundwater level and pore water pressure can all react to the stability of the landslide body. In this paper, the experimental data can be used to study the correlation between different depths of deep displacement and surface displacement, groundwater level and pore water pressure. First, qualitative research is carried out in the form of graphs; then, quantitative research is carried out by using Pearson correlation coefficient method using MATLAB software; finally, the model is established by multivariate linear regression method. Through comparative analysis, it can be found that the established multivariate linear regression model can well predict surface displacement, groundwater level, pore water pressure and deep displacement.
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Acknowledgements
This study is supported by the National Natural Science Foundation of China (grant no. 41674036).
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Li, G., Ke, F. (2020). Correlation Analysis Between Deep Displacement and Multi-source Landslide Monitoring Data. In: Shen, J., Chang, YC., Su, YS., Ogata, H. (eds) Cognitive Cities. IC3 2019. Communications in Computer and Information Science, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6113-9_51
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DOI: https://doi.org/10.1007/978-981-15-6113-9_51
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