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Displacement prediction of water-induced landslides using a recurrent deep learning model.
- Source :
-
European Journal of Environmental & Civil Engineering . May2023, Vol. 27 Issue 7, p2460-2474. 15p. - Publication Year :
- 2023
-
Abstract
- Displacement prediction is a direct and effective method for mitigating geohazards. Due to the influence of rainfall and reservoir water level variations, landslides often display step-like deformations with an increasing trend and periodic fluctuation, indicating long-term memory in displacement time series. Traditional data-driven methods are mostly suitable for short-term prediction, and extra data processing is applied to solve this problem. This paper proposes a novel deep learning-based displacement prediction method using long short-term memory (LSTM) networks. Based on open-source frameworks for deep learning, namely, Keras and TensorFlow, a detailed implementation of displacement prediction is proposed and illustrated. The Baishuihe landslide, a typical landslide with long-term monitoring, is taken as a case study, and both single-factor and multi-factor predictions are performed. The results indicate that multi-factor prediction can reduce overfitting and improve accuracy. Compared with the existing method, the multi-factor deep-learning model displays better performance. This study indicates that the LSTM-based deep-learning model is suitable and convenient for displacement prediction and has broad prospects in safety monitoring of water-induced landslides. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19648189
- Volume :
- 27
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- European Journal of Environmental & Civil Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 163520706
- Full Text :
- https://doi.org/10.1080/19648189.2020.1763847