Time Series Prediction Model of Landslide Displacement Using Mean-Based Low-Rank Autoregressive Tensor Completion

• Published in: Applied sciences 2023-04, Vol.13 (8), p.5214
• Main Authors: Wang, Chenhui, Zhao, Yijiu
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Automation; autoregressive norm; Data analysis; Data collection; Data processing; Decomposition; Displacement; displacement prediction; Economic development; Eigenvalues; Extreme weather; Landslides; Landslides & mudslides; Machine learning; Missing data; Monitoring; Monitoring systems; Neural networks; Prediction models; Sensors; tensor completion; Time series
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13085214

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction.