Predicting Topographic Effect Multipliers in Complex Terrain With Shallow Neural Networks

Predicting Topographic Effect Multipliers in Complex Terrain With Shallow Neural Networks

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 s...

Full description

Bibliographic Details
Main Authors: J. X. Santiago-Hernández, A. Román Santiago, R. A. Catarelli, B. M. Phillips, L. D. Aponte-Bermúdez, F. J. Masters
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-05-01
Series:Frontiers in Built Environment
Subjects:
complex topography
neural network
boundary layer wind tunnel
complex terrain
wind speedup
Online Access:https://www.frontiersin.org/articles/10.3389/fbuil.2022.762054/full

Internet

https://www.frontiersin.org/articles/10.3389/fbuil.2022.762054/full

Similar Items