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Process-Based and Data-Based Storm Surge Models for Rhode Island Coastal Flooding within the STORMTOOLS Framework

机译:STORMTOOLS框架内基于过程和基于数据的风暴潮模型,用于罗德岛沿海洪水

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Here, we present two approaches for storm surge forecasting in coastal areas of Rhode Island: a regional ADCIRC model, and artificial intelligence (AI). Recent numerical modeling results published by north atlantic coast comprehensive study (NACSS) were employed as the basis. Using a downscaling approach, a high resolution ADCIRC hydrodynamic model was developed and interfaced with the NACCS model along the open boundaries. Although this model could effectively predict storm surges for the past historical/synthetic storms, it was numerically very expensive to provide boundary information for any storm surge forecasting scenario. To address this issue, an efficient AI data-based model was developed. The AI model predicts the storm surge using tropical storm parameters (i.e. central pressure, radius to maximum winds, forward velocity, and storm track). The AI model was validated using a set of randomly selected synthetic storms as well as real extreme storms in this region, and the performance was found satisfactory.
机译:在这里,我们提出了两种在罗德岛沿海地区进行风暴潮预报的方法:区域ADCIRC模型和人工智能(AI)。北大西洋沿岸综合研究(NACSS)发布的最新数值模拟结果被用作基础。使用缩减方法,开发了高分辨率ADCIRC水动力模型,并沿开放边界与NACCS模型对接。尽管此模型可以有效地预测过去的历史/合成风暴的风暴潮,但是为任何风暴潮预测场景提供边界信息在数值上非常昂贵。为了解决这个问题,开发了一种有效的基于AI数据的模型。 AI模型使用热带风暴参数(即中心压力,最大风的半径,前进速度和风暴轨迹)预测风暴潮。使用一组随机选择的合成风暴以及该地区的实际极端风暴对AI模型进行了验证,并发现性能令人满意。

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