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Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

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JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 2024年第10期16卷 4088-4104页

作者： Yang, Beibei Liu, Zhongqiang Lacasse, Suzanne Liang, Xin Yantai Univ Sch Civil Engn Yantai 264005 Peoples R China Norwegian Geotech Inst N-0855 Oslo Norway China Univ Geosci Fac Engn Wuhan 430074 Peoples R China

Since the impoundment of Three gorges reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: longterm deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article und

关键词： Landslide Deformation characteristics Triggering factor Data mining Three gorges reservoir 3 gorges reservoir REGION

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Data-augmented landslide displacement prediction using generative adversarial network

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JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 2024年第10期16卷 4017-4033页

作者： Ge, Qi Li, Jin Lacasse, Suzanne Sun, Hongyue Liu, Zhongqiang Nanjing Forestry Univ Coll Civil Engn Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Inst Artificial Intelligence Nanjing Peoples R China Norwegian Geotech Inst Dept Nat Hazards Oslo Norway Zhejiang Univ Ocean Coll Hangzhou Peoples R China

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three gorges reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50

关键词： Machine learning (ML) Time series Generative adversarial network (GAN) Three gorges reservoir (TGR) Landslide displacement prediction MEMORY NEURAL-NETWORK TIME-SERIES ANALYSIS 3 gorges reservoir MACHINE MODEL DECOMPOSITION ALGORITHMS

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