A DLM-LSTM Framework for North-South Land Deformation Trend Analysis from Low-Cost GPS Sensor Time Series. (6th June 2018)

• Record Type: Journal Article
• Authors: Pu, Fangling; Xu, Zhaozhuo; Chen, Hongyu; Xu, Xin; Chen, Nengcheng
• Other Names: Pradhan Biswajeet Academic Editor.
• Is Part Of: Journal of sensors. Volume 2018(2018)
• Publication Date: 2018-06-06
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2018/3054295
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Landslides endanger regular industrial production and human safety. Displacement trend analysis gives us an explicit way to observe and forecast landslides. Although satellite-borne remote sensing methods such as synthetic aperture radar have gradually replaced manual measurement in detecting deformation trends, they fail to observe displacement in a north-south direction. Wireless low-cost GPS sensors have been developed to assist remote sensing methods in north-south deformation monitoring because of their high temporal resolution and wide usage. In our paper, a DLM-LSTM framework is developed to extract and predict north-south land deformation trends from meter accuracy GPS receivers. A dynamic linear model is introduced to model the relation between measurement and the state vector, including the trend, periodic variation, and autoregressive factors in a discontinuous low-cost latitude time series. The deformation trend with submeter-level accuracy is extracted by a Kalman filter and smoother. With validated input as in previous work, the power of an LSTM network is also shown in its ability to predict deformation trends in submeter-level accuracy. A submeter-level deformation trend is detected from wireless low-cost GPS sensors with meter-level navigation error. The framework will have broad application prospects in geological disaster monitoring.