首页|Data-augmented landslide displacement prediction using generative adversarial network

Data-augmented landslide displacement prediction using generative adversarial network

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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 net-works(GANs),a recent advance in generative artificial intelligence(Al),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％on the illustration cases)maximizes the improvements.This also demonstrates the robustness and effectiveness of sup-plementing training data for dynamic models to obtain better results.By using the powerful generative Al approach,RGAN-LS can generate high-fidelity synthetic landslide data.This is critical for improving the performance of advanced ML models in predicting landslide displacement,particularly when there are limited training data.Additionally,this approach has the potential to expand the use of generative Al in geohazard risk management and other research areas.

Machine learning(ML)Time seriesGenerative adversarial network(GAN)Three Gorges reservoir(TGR)Landslide displacement prediction

Qi Ge、Jin Li、Suzanne Lacasse、Hongyue Sun、Zhongqiang Liu

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College of Civil Engineering,Nanjing Forestry University,Nanjing,China

Institute of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing,China

Department of Natural Hazards,Norwegian Geotechnical Institute,Oslo,Norway

Ocean College,Zhejiang University,Hangzhou,China

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National Field Observation and Research Station of Landslides in the Three Gorges Reservoir area of the Yangtze RiverNatural Science Foundation of Jiangsu ProvinceState Key Program of the National Natural Science Foundation of ChinaNational Natural Science Foundation of China

BK202204214223070282302352

2024

10.1016/j.jrmge.2024.01.003

岩石力学与岩土工程学报(英文版)
中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD
影响因子：0.404
ISSN：1674-7755
年,卷(期)：2024.16(10)