Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction

Xu, Xovee; Zhong, Ting; Zhang, Fan; Li, Rongfan; Trajcevski, Goce; Meng, Qinggang

doi:10.1109/tcyb.2023.3291049

Cited by 7 publications

(1 citation statement)

References 46 publications

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“…For these challenges, recent research suggests that deep learning techniques hold promise by directly capturing non‐linear patterns within the data, potentially leading to improved ground deformation forecasting (Xu et al., 2023). Within the broader domain of geosciences, deep neural networks have demonstrated success in forecasting various physical processes by identifying inherent, predictable patterns within data sets (Labe & Barnes, 2022; Mayer & Barnes, 2021; Toms et al., 2021; B. Li et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

Ma,

Mei,

2024

Journal of Geophysical Research: Machine Learning and Computati

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Ground deformation, a critical indicator of geohazard evolution, exhibits both seasonal fluctuations and long‐term trend changes. This study explores interpretable deformation forecasting using an explainable deep learning approach, utilizing field observation data to extract and characterize these critical features from time series. We demonstrate that extracting and utilizing shared, similar seasonal and trend features across multi‐point observations significantly enhances ground deformation forecasting accuracy. Notably, the extracted seasonal features reveal the dominant role of the hydrological response in deformation at seasonal timescales, providing insights into the underlying source of predictability. This proof‐of‐concept study highlights the promise of interpretable ground deformation forecasting, with applications expected to encompass landslide behavior, volcanism, or seismic activity.

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Section: Introductionmentioning

confidence: 99%

Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

Ma,

Mei,

2024

Journal of Geophysical Research: Machine Learning and Computati

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Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities

Ma,

Mei,

2024

Artif Intell Rev

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Data mining and analysis are critical for preventing or mitigating natural hazards. However, data availability in natural hazard analysis is experiencing unprecedented challenges due to economic, technical, and environmental constraints. Recently, generative deep learning has become an increasingly attractive solution to these challenges, which can augment, impute, or synthesize data based on these learned complex, high-dimensional probability distributions of data. Over the last several years, much research has demonstrated the remarkable capabilities of generative deep learning for addressing data-related problems in natural hazards analysis. Data processed by deep generative models can be utilized to describe the evolution or occurrence of natural hazards and contribute to subsequent natural hazard modeling. Here we present a comprehensive review concerning generative deep learning for data generation in natural hazard analysis. (1) We summarized the limitations associated with data availability in natural hazards analysis and identified the fundamental motivations for employing generative deep learning as a critical response to these challenges. (2) We discuss several deep generative models that have been applied to overcome the problems caused by limited data availability in natural hazards analysis. (3) We analyze advances in utilizing generative deep learning for data generation in natural hazard analysis. (4) We discuss challenges associated with leveraging generative deep learning in natural hazard analysis. (5) We explore further opportunities for leveraging generative deep learning in natural hazard analysis. This comprehensive review provides a detailed roadmap for scholars interested in applying generative models for data generation in natural hazard analysis.

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Recognition of Multi-channel Uncertain Digital Information Based on the Representation of Multi-step Platform Fuzzy Cell Number

Wang,

Shi

2024

Int. J. Fuzzy Syst.

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Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction

Cited by 7 publications

References 46 publications

Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities

Recognition of Multi-channel Uncertain Digital Information Based on the Representation of Multi-step Platform Fuzzy Cell Number

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