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

Ma, Zhengjing; Mei, Gang; Xu, Nengxiong

doi:10.1007/s10462-024-10764-9

Cited by 3 publications

References 252 publications

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" Solar energy radiation predication based in regional satellite images and data"

Attya,

Abo-Seida,

Abdulkader

et al. 2024

Preprint

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Accurate solar radiation prediction is crucial for optimizing solar energy systems. There are two types of data can be used to predict the solar radiation such as the satellite images and the tabular satellite data. This research aims to create a more comprehensive picture of the factors influencing solar radiation. This research presents a hybrid methodology to predict the solar radiation from both satellite images and satellite data. The methodology basis on two datasets; the first data set contains tabular data, and the second dataset contains satellite images. The framework divides into two paths; the first path take the input as the satellite images; this stages contains three steps; the first step is removing noise using latent diffusion model, the second step is about pixel imputation using a modified RF + Identity GAN (this model contains two modification the first modification is adding the identity block to solve mode collapse problem in the GANs and the second modification is to add the 8-connected pixel to generate a value of missing pixel near to the real missed pixel. The third step in the first path is about using the self-organizing map to identify the special informative in the satellite image. The second path take the input as tabular data and use the diffusion model to impute the missing data in the tabulated data. Finally, we merge the two path and use feature selection to be as input for the LSTM for solar radiation predictions. The experiments done prove the efficiency of the used stage such as missing pixel imputation, removing noise, missing data imputation and prediction using LSTM when compared with other available techniques. The experiments also prove the enhancement of all prediction model after adding two paths before the prediction step.

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" Solar energy radiation predication based in regional satellite images and data"

Attya,

Abo-Seida,

Abdulkader

et al. 2024

Preprint

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Leveraging Generative AI Synthetic and Social Media Data for Content Generalizability to Overcome Data Constraints in Vision Deep Learning

Alipour,

Gallegos

2024

Preprint

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Generalizing deep learning models across diverse content types is a persistent challenge in domains like Facial Emotion Recognition (FER), where datasets often fail to reflect the wide range of emotional responses triggered by different stimuli. This study addresses the issue of content generalizability by comparing FER model performance between models trained on video data collected in a controlled laboratory environment, data extracted from a social media platform (YouTube), and synthetic data generated using Generative Adversarial Networks. The videos focus on facial reactions to advertisements, and the integration of these different data sources seeks to address underrepresented advertisement genres, emotional reactions, and individual diversity. Our FER models leverage Convolutional Neural Networks Xception architecture, which is fine-tuned using category based sampling. This ensures training and validation data represent diverse advertisement categories, while testing data includes novel content to evaluate generalizability rigorously. Precision-recall curves and ROC-AUC metrics are used to assess performance. Results indicate a 7% improvement in accuracy and a 12% increase in precision-recall AUC when combining real-world social media and synthetic data, demonstrating reduced overfitting and enhanced content generalizability. These findings highlight the effectiveness of integrating synthetic and real-world data to build FER systems that perform reliably across more diverse and representative content.

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Generating Stochastic Structural Planes Using Statistical Models and Generative Deep Learning Models: A Comparative Investigation

Meng,

Xu,

Zhu

et al. 2024

Mathematics

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Structural planes are one of the key factors controlling the stability of rock masses. A comprehensive understanding of the spatial distribution characteristics of structural planes is essential for accurately identifying key blocks, analyzing rock mass stability, and addressing various rock engineering challenges. This study compares the effectiveness of four stochastic structural plane generation methods—the Monte Carlo method, the Copula-based method, generative adversarial networks (GAN), and denoised diffusion models (DDPM)—in generating stochastic structural planes and capturing potential correlations between structural plane parameters. The Monte Carlo method employs the mean and variance of three parameters of the measured factual structural planes to generate data that follow the same distributions. The other three methods take the entire set of measured factual structural planes as the overall input to generate structural planes that exhibit the same probability distributions. Five sets of structural planes on four rock slopes in Norway are examined as an example. The validation and analysis were performed using histogram comparison, data feature comparison, scatter plot comparison, and linear regression analysis. The results show that the Monte Carlo method fails to capture the potential correlation between the dip direction and dip angle despite the best fit to the measured factual structural planes. The Copula-based method performs better with smaller datasets, and GAN and DDPM are better at capturing the correlation of measured factual structural planes in the case of large datasets. Therefore, in the case of a limited number of measured structural planes, it is advisable to employ the Copula-based method. In scenarios where the dataset is extensive, the deep generative model is recommended due to its ability to capture complex data structures. The results of this study can be utilized as a valuable point of reference for the accurate generation of stochastic structural planes within rock masses.

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

Cited by 3 publications

References 252 publications

" Solar energy radiation predication based in regional satellite images and data"

" Solar energy radiation predication based in regional satellite images and data"

Leveraging Generative AI Synthetic and Social Media Data for Content Generalizability to Overcome Data Constraints in Vision Deep Learning

Generating Stochastic Structural Planes Using Statistical Models and Generative Deep Learning Models: A Comparative Investigation

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