Tropical Cyclone Wind Field Reconstruction for Hazard Estimation via Bayesian Hierarchical Modeling With Neural Network

Earth and Space Science, Год журнала: 2024, Номер 11(12)

Опубликована: Дек. 1, 2024

Abstract Tropical cyclones (TCs) are one of the biggest threats to life and property around world. Accurate estimation TC wind hazard requires catastrophic TCs having a very long return period spanning up thousands years. Since reliable data available only for recently decades, stochastic modeling simulation turned out be an effective approach achieve more stable estimates. In common practice, hundreds synthetic generated first, then fields reconstructed along tracks estimation. A Bayesian hierarchical reconstruction field is proposed. modified Rankine vortex adopted as model, which four free parameters modeled simultaneously through multi‐output neural network latent process field. The finally represented, spatially temporally, by set weights, model averaging technique used parameter reconstruction, based on ensemble maximum posteriori estimates weights. Together with previously proposed algorithm simulation, two‐stage scheme has been formed, best‐track thus highly consistent. Application this offshore waters in western North Pacific basin shows inspiring performance great flexibility various purposes

Язык: Английский

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Prediction of Total Phosphorus Concentration in Canals by GAT-Informer Model Based on Spatiotemporal Correlations

Water, Год журнала: 2024, Номер 17(1), С. 12 - 12

Опубликована: Дек. 24, 2024

The accurate prediction of total phosphorus (TP) is crucial for the early detection water quality eutrophication. However, predicting TP concentrations among canal sites challenging due to their complex spatiotemporal dependencies. To address this issue, study proposes a GAT-Informer method based on correlations predict in Beijing–Hangzhou Grand Canal Basin Changzhou City. begins by creating feature sequences each site time lag relationship concentration between sites. It then constructs graph data combining real river distance and correlation sequences. Next, spatial features are extracted fusing node using attention (GAT) module. employs Informer network, which uses sparse mechanism extract temporal efficiently simulating model was evaluated R2, MAE, RMSE, with experimental results yielding values 0.9619, 0.1489%, 0.1999%, respectively. exhibits enhanced robustness superior predictive accuracy comparison traditional models.

Язык: Английский

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Prediction of water levels in large reservoirs base on optimization of deep learning algorithms

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 28, 2024

Язык: Английский

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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed

Опубликована: Янв. 1, 2024

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Язык: Английский

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Modeling Dissolved Oxygen Under Data Scarcity Situation Using Time-Series Generative Adversarial Network Combined with Long Short-Term Memory Network

Опубликована: Янв. 1, 2024

Язык: Английский

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