19 Jan 2016
19 Jan 2016
Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area
- 1Faculty of Mechanical and Electronic Information, China University of Geosciences, Wuhan, 430074, China
- 2Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269-3037, USA
- 1Faculty of Mechanical and Electronic Information, China University of Geosciences, Wuhan, 430074, China
- 2Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269-3037, USA
Abstract. Rainfall is one of the most important factors controlling landslide deformation and failure. State-of-art rainfall data collection is a common practice in modern landslide research world-wide. Nevertheless, in spite of the availability of high-accuracy rainfall data, it is not a trivial process to diligently incorporate rainfall data in predicting landslide stability due to large quantity, tremendous variety, and wealth multiplicity of rainfall data. Up to date, most of the pre-process procedure of rainfall data only use mean value, maxima and minima to characterize the rainfall feature. This practice significantly overlooks many important and intrinsic features contained in the rainfall data. In this paper, we employ cluster analysis (CA)-based feature analysis to rainfall data for rainfall feature extraction. This method effectively extracts the most significant features of a rainfall sequence and greatly reduced rainfall data quantities. Meanwhile it also improves rainfall data availability.
For showing the efficiency of using the CA characterized rainfall data input, we present three schemes to input rainfall data in back propagation (BP) neural network to forecast landslide displacement. These three schemes are: the original daily rainfall, monthly rainfall, and CA extracted rainfall features. Based on the examination of the root mean square error (RMSE) of the landslide displacement prediction, it is clear that using the CA extracted rainfall features input significantly improve the ability of accurate landslide prediction.
Y. Liu and L. Liu


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SC1: 'Revision of the pubblication year', Massimo Melillo, 22 Jan 2016
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AC1: 'The last updated figure is for Fig 6. not Fig. 1.', Lanbo Liu, 22 Jan 2016
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RC1: 'Referee Comment "Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area"', Anonymous Referee #1, 29 Feb 2016
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AC2: 'Responses to the Comments from Referee #1', Lanbo Liu, 17 Apr 2016
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AC2: 'Responses to the Comments from Referee #1', Lanbo Liu, 17 Apr 2016
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RC2: 'Review: Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area. Submitted to NHESS journal, MS No.: nhess-2015-320. Author: Y. Liu and L. Liu', Anonymous Referee #2, 25 Mar 2016
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AC3: 'Responses to Comments from Referee #2', Lanbo Liu, 17 Apr 2016
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AC3: 'Responses to Comments from Referee #2', Lanbo Liu, 17 Apr 2016
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AC4: 'The revised manuscript after incorporation of the changes in response to the Comments from Referees #1 and #2.', Lanbo Liu, 17 Apr 2016


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SC1: 'Revision of the pubblication year', Massimo Melillo, 22 Jan 2016
-
AC1: 'The last updated figure is for Fig 6. not Fig. 1.', Lanbo Liu, 22 Jan 2016
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RC1: 'Referee Comment "Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area"', Anonymous Referee #1, 29 Feb 2016
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AC2: 'Responses to the Comments from Referee #1', Lanbo Liu, 17 Apr 2016
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AC2: 'Responses to the Comments from Referee #1', Lanbo Liu, 17 Apr 2016
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RC2: 'Review: Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area. Submitted to NHESS journal, MS No.: nhess-2015-320. Author: Y. Liu and L. Liu', Anonymous Referee #2, 25 Mar 2016
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AC3: 'Responses to Comments from Referee #2', Lanbo Liu, 17 Apr 2016
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AC3: 'Responses to Comments from Referee #2', Lanbo Liu, 17 Apr 2016
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AC4: 'The revised manuscript after incorporation of the changes in response to the Comments from Referees #1 and #2.', Lanbo Liu, 17 Apr 2016
Y. Liu and L. Liu
Y. Liu and L. Liu
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