Surge-NF: Neural Fields inspired peak storm surge surrogate modeling with multi-task learning and positional encoding

IF 4.2 2区 工程技术 Q1 ENGINEERING, CIVIL
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引用次数: 0

Abstract

Storm surges pose a significant threat to coastal communities, necessitating rapid and precise storm surge prediction methods for long-time risk assessment and emergency management. High-fidelity numerical models such as ADCIRC provide accurate storm surge simulations but are computationally expensive. Surrogate models have emerged as an alternative option to alleviate the computational burden by learning from available numerical datasets. However, existing surrogate models face challenges in capturing the highly non-stationary and non-linear patterns of storm surges, resulting in over-smoothed response surfaces. Moreover, the dry–wet status of nearshore nodes has not been informatively considered in the training process.

This study proposes Surge-NF, a novel point-based surrogate model inspired by Neural Fields (NF) from computer graphics. Surge-NF introduces two key innovations. A positional encoding module is proposed to mitigate over-smoothing of high-frequency peak storm surge spatial dependencies. A multi-task learning framework is proposed to simultaneously learn and predict the dry–wet status and peak surge values, leveraging task dependencies to improve prediction accuracy and data efficiency. We evaluate Surge-NF on the NACCS database with comparison to state-of-the-art alternative surrogate models. Surge-NF consistently reduces RMSE/MAE by 50% and achieves 4–5 times computational cost gain over baselines, requiring only 50 training storms to produce accurate predictions. The complementary benefits of the positional encoding and multi-task learning modules are evident from the improved prediction capability with their combined use.

Overall, Surge-NF represents a significant advancement in storm surge surrogate modeling, offering its novel and unique ability to capture high-frequency spatial variations and leverage task dependencies. It has the potential to greatly enhance storm surge risk assessment and emergency response management, enabling effective decision-making and mitigation strategies to safeguard coastal communities from the devastating impacts of storm surges.

Surge-NF:受神经场启发,利用多任务学习和位置编码进行风暴潮峰值代用建模
风暴潮对沿海社区构成重大威胁,因此需要采用快速、精确的风暴潮预测方法来进行长 期风险评估和应急管理。ADCIRC 等高保真数值模式可以提供精确的风暴潮模拟,但计算成本高昂。代用模型通过学习现有的数值数据集,成为减轻计算负担的另一种选择。然而,现有的代用模式在捕捉风暴潮的高度非稳态和非线性模式方面面临挑战,导致响应面过于平滑。此外,在训练过程中,近岸节点的干湿状态也未被考虑在内。本研究提出了 Surge-NF,这是一种基于点的新型代用模型,其灵感来自计算机图形学中的神经场(NF)。Surge-NF 引入了两项关键创新。提出了一个位置编码模块,以减轻对高频风暴潮峰值空间依赖性的过度平滑。我们还提出了一个多任务学习框架,用于同时学习和预测干湿状态和峰值浪涌值,利用任务相关性来提高预测精度和数据效率。我们在 NACCS 数据库中对 Surge-NF 进行了评估,并与最先进的替代模型进行了比较。与基线相比,Surge-NF 的 RMSE/MAE 持续降低了 50%,计算成本提高了 4-5 倍,只需 50 次训练风暴就能做出准确预测。总之,Surge-NF 代表了风暴潮代理建模的重大进步,它具有捕捉高频空间变化和利用任务依赖性的新颖独特能力。总之,Surge-NF 具有捕捉高频空间变化和利用任务依赖性的新颖独特能力,是风暴潮代用模型的重大进步。它有可能极大地加强风暴潮风险评估和应急响应管理,使有效的决策和减灾战略成为可能,从而保护沿海社区免受风暴潮的破坏性影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Coastal Engineering
Coastal Engineering 工程技术-工程:大洋
CiteScore
9.20
自引率
13.60%
发文量
0
审稿时长
3.5 months
期刊介绍: Coastal Engineering is an international medium for coastal engineers and scientists. Combining practical applications with modern technological and scientific approaches, such as mathematical and numerical modelling, laboratory and field observations and experiments, it publishes fundamental studies as well as case studies on the following aspects of coastal, harbour and offshore engineering: waves, currents and sediment transport; coastal, estuarine and offshore morphology; technical and functional design of coastal and harbour structures; morphological and environmental impact of coastal, harbour and offshore structures.
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