Journal
LANDSLIDES
Volume 17, Issue 3, Pages 693-707Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10346-019-01312-6
Keywords
Landslide deformation; Wavelet analysis; Deep belief network; EWMA control charts
Funding
- Innovative Research Group Project of the National Natural Science Foundation of China [41521002]
- Major Research Plan of the National Natural Science Foundation of China [41790445]
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The accurate modeling and predicting of landslide deformation is crucial to the prevention of landslide hazard. This paper presents a pioneering study of modeling and predicting the reservoir landslide displacement with deep learning algorithm. A data-driven framework using deep belief network and control chart has been introduced to explore the temporal patterns of displacement and potential of identifying seasonal faster displacement. First, the continuous wavelet analysis has been applied to decompose the time-series precipitation, reservoir water level, and displacement into seasonal and residual components. Second, the deep belief network has been constructed to predict the future displacement. Third, it utilizes the exponentially weighted moving average (EWMA) control chart to derive the boundaries as alarm conditions of seasonal faster displacement. A group of tests are conducted to compare the performance of the deep belief network with other state-of-the-art machine learning algorithms. Computational results demonstrated the effectiveness of the deep belief network in extracting highly non-linear data features. In addition, the advantage of utilizing control charts has been further validated by the accuracy of examining the seasonal faster displacement based on the case study in Baishuihe landslide in Three Gorges Reservoir, China.
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