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Image Credit: Arxiv

Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach

  • Machine learning algorithms are being used to reduce bias in wind gust predictions, but they still struggle with accurately predicting high gusts.
  • A new approach called evidential neural network (ENN) is introduced to address the issue of uncertainty quantification (UQ) in gust predictions by leveraging atmospheric variables from the Weather Research and Forecasting (WRF) model.
  • Explainable AI techniques identified key features contributing to higher uncertainty in gust predictions, which were found to be strongly correlated with storm intensity and spatial gust gradients.
  • ENN demonstrated a 47% reduction in RMSE compared to WRF, allowing for the construction of gust prediction intervals without the need for an ensemble, and successfully capturing at least 95% of observed gusts at 179 out of 266 stations.

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