Paper Info
Title
A DLM-LSTM Framework for North-South Land Deformation Trend Analysis from Low-Cost GPS Sensor Time Series.
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.
Year
DOI
Venue
2018
10.1155/2018/3054295
JOURNAL OF SENSORS
Field
DocType
Volume
Autoregressive model,Trend analysis,State vector,Synthetic aperture radar,Deformation monitoring,Remote sensing,Electronic engineering,Kalman filter,Global Positioning System,Engineering,Temporal resolution
Journal
2018
ISSN
Citations 
PageRank 
1687-725X
0
0.34
References 
Authors
6
5
Name
Order
Citations
PageRank
Fangling Pu1357.24
Zhaozhuo Xu2102.91
Hongyu Chen310.69
Xin Xu416240.08
Nengcheng Chen527041.34