Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model

Autor:	Jing Wang, Guigen Nie, Shengjun Gao, Shuguang Wu, Haiyang Li, Xiaobing Ren
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	GNSS time series analysis landslide displacement prediction attention mechanism deep learning Science
Zdroj:	Remote Sensing, Vol 13, Iss 6, p 1055 (2021)
Druh dokumentu:	article
ISSN:	2072-4292
DOI:	10.3390/rs13061055
Popis:	The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4f829a16d5064dd2bcc82d545db76585 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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