Open AccessApplication of an adaptive weighted estimation fusion algorithm in landslide deformation monitoring data processing
Author(s) -
Yaohui Zhang,
Wang Li,
Shu Zhe Bao,
Haibing He,
Long Li
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/570/6/062045
Subject(s) - landslide , gnss applications , algorithm , sensor fusion , fuse (electrical) , computer science , precise point positioning , data processing , deformation monitoring , data mining , global positioning system , artificial neural network , displacement (psychology) , satellite , deformation (meteorology) , remote sensing , artificial intelligence , geology , seismology , engineering , psychology , telecommunications , oceanography , aerospace engineering , electrical engineering , psychotherapist , operating system
For early warning and forecast of landslide disasters, several types of sensors are used to monitor landslide deformation at different locations. In practical applications, a simple comparative analysis of the data collected at each monitoring point cannot fully use the information provided by these data. In this contribution, the use of an adaptive weighted estimation fusion algorithm is implemented to fuse the landslide deformation data collected by the Global Navigation Satellite System (GNSS) receiver and the displacement meter. The fused data are utilized for a discriminant analysis of the landslide stage. In contrast to the fusion results obtained using the original monitoring data and a back-propagation neural network, the results obtained using the adaptive weighted estimation fusion algorithm reveal that the one-sidedness of the information can be overcome using a single monitoring method and single monitoring point data. Moreover, the algorithm can provide a reliable criterion for the analysis of landslide deformation.