Predicting Topographic Effect Multipliers in Complex Terrain With Shallow Neural Networks
Predicting Topographic Effect Multipliers in Complex Terrain With Shallow Neural Networks
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DOI：
10.3389/fbuil.2022.762054
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
J. Santiago-Hernandez;A. R. Santiago;R. A. Catarelli;B. M. Phillips;L. D. Aponte-Bermúdez;F. Masters;Yanlin Guo;Guowei Qian
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文献类型：
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作者：
J. Santiago-Hernandez;A. R. Santiago;R. A. Catarelli;B. M. Phillips;L. D. Aponte-Bermúdez;F. Masters;Yanlin Guo;Guowei Qian
本研究应用计算效率高的浅层神经网络直接从复杂地形（如山区）的数字高程数据中预测地形效应乘数。数据来自于波多黎各大陆及其自治岛屿六个区域的地面风场边界层风洞（BLWT）模拟。结果表明，线性回归模型的改进，即使是计算效率低的神经元计数和单隐藏层模型。本文提出了一个全球BLWT数据图集的发展，为不同范围的地形和表面粗糙度条件下预测地形风加速的方法的发展提供信息。它还确定了知识差距，可能会阻止从不同的BLWT实验设计收集的数据的标准化。
This study applies computationally efficient shallow neural networks to predict topographic effect multipliers directly from digital elevation data obtained from complex terrain, such as mountainous areas. Data were obtained from boundary layer wind tunnel (BLWT) modeling of surface wind flow over six regions in mainland Puerto Rico and its municipal islands. The results demonstrate an improvement over linear regression models, even for computationally efficient low neuron count and single hidden layer models. The paper proposes the development of a global BLWT data atlas to inform development of methods to predict topographic wind speedup for a diverse range of topography and surface roughness conditions. It also identifies knowledge gaps that could prevent standardization of data collected from different BLWT experimental designs.