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HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks

Noujoud Nadera, Hadi Majed, Stefanos Giaremis, Rola El Osta, Clint Dawson, Carola Kaiser, Hartmut Kaiser

TL;DR

HURRI-GAN, a novel AI-driven approach that augments the results produced by physical simulation models using time series generative adversarial networks (TimeGAN) to compensate for systemic errors of the physical model, is introduced, thus reducing the necessary mesh size and runtime without loss in forecasting accuracy.

Abstract

The coastal regions of the eastern and southern United States are impacted by severe storm events, leading to significant loss of life and properties. Accurately forecasting storm surge and wind impacts from hurricanes is essential for mitigating some of the impacts, e.g., timely preparation of evacuations and other countermeasures. Physical simulation models like the ADCIRC hydrodynamics model, which run on high-performance computing resources, are sophisticated tools that produce increasingly accurate forecasts as the resolution of the computational meshes improves. However, a major drawback of these models is the significant time required to generate results at very high resolutions, which may not meet the near real-time demands of emergency responders. The presented work introduces HURRI-GAN, a novel AI-driven approach that augments the results produced by physical simulation models using time series generative adversarial networks (TimeGAN) to compensate for systemic errors of the physical model, thus reducing the necessary mesh size and runtime without loss in forecasting accuracy. We present first results in extrapolating model bias corrections for the spatial regions beyond the positions of the water level gauge stations. The presented results show that our methodology can accurately generate bias corrections at target locations spatially beyond gauge stations locations. The model's performance, as indicated by low root mean squared error (RMSE) values, highlights its capability to generate accurate extrapolated data. Applying the corrections generated by HURRI-GAN on the ADCIRC modeled water levels resulted in improving the overall prediction on the majority of the testing gauge stations.

HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks

TL;DR

HURRI-GAN, a novel AI-driven approach that augments the results produced by physical simulation models using time series generative adversarial networks (TimeGAN) to compensate for systemic errors of the physical model, is introduced, thus reducing the necessary mesh size and runtime without loss in forecasting accuracy.

Abstract

The coastal regions of the eastern and southern United States are impacted by severe storm events, leading to significant loss of life and properties. Accurately forecasting storm surge and wind impacts from hurricanes is essential for mitigating some of the impacts, e.g., timely preparation of evacuations and other countermeasures. Physical simulation models like the ADCIRC hydrodynamics model, which run on high-performance computing resources, are sophisticated tools that produce increasingly accurate forecasts as the resolution of the computational meshes improves. However, a major drawback of these models is the significant time required to generate results at very high resolutions, which may not meet the near real-time demands of emergency responders. The presented work introduces HURRI-GAN, a novel AI-driven approach that augments the results produced by physical simulation models using time series generative adversarial networks (TimeGAN) to compensate for systemic errors of the physical model, thus reducing the necessary mesh size and runtime without loss in forecasting accuracy. We present first results in extrapolating model bias corrections for the spatial regions beyond the positions of the water level gauge stations. The presented results show that our methodology can accurately generate bias corrections at target locations spatially beyond gauge stations locations. The model's performance, as indicated by low root mean squared error (RMSE) values, highlights its capability to generate accurate extrapolated data. Applying the corrections generated by HURRI-GAN on the ADCIRC modeled water levels resulted in improving the overall prediction on the majority of the testing gauge stations.
Paper Structure (17 sections, 7 equations, 6 figures, 4 tables)

This paper contains 17 sections, 7 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Data and Methodology
  3. Overview
  4. Data
  5. Clustering
  6. Preprocessing
  7. GenAI models
  8. Overview of TimeGAN Components
  9. Proposed HURRI-GAN structure
  10. Evaluation Parameters and Computational Details
  11. Results and Discussion
  12. LSTM-based model results
  13. Hyperparameter tuning
  14. Extrapolation analysis
  15. Inference Performance Analysis for Extrapolation with HURRI-GAN
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Overview of the methodology framework: (A) Data Pre-processing phase includes offset extraction using Eq. \ref{['eq:offsets']}, data cleaning, and normalization. (B) Modeling phase involves the structure of the TimeGAN components and model evaluation using regression metrics. (C) Output phase includes the final application of the pre-trained model for bias extrapolation. The generated offsets are then used to correct the forecasted data using Eq. \ref{['eq:corrected']}.
  • Figure 2: Station clustering for Hurricane Harvey (2017). This map illustrates the clustering of stations based on their coordinates. A total of 24 clusters were identified, with each color representing a different cluster.
  • Figure 3: RMSE values for the testing stations across different hurricanes. The map for each hurricane shows the locations of the testing stations along with their corresponding RMSE values (in feet), indicated by the color gradient. The considered hurricanes are Harvey (2017), Hermine (2016), Ian (2022), Ida (2021), Idalia (2023), and Matthew (2016) and the considered agencies are NOAA, USACE, USGS, TCOON, and PRSN. The hurricane paths are outlined in red.
  • Figure 4: RMSE distribution of the extrapolated offsets with the HURRI-GAN model in the testing stations by hurricane and agency. The considered hurricanes are Harvey (2017), Hermine (2016), Ian (2022), Ida (2021), Idalia (2023), and Matthew (2016) and the considered agencies are NOAA, USACE, USGS, TCOON, and PRSN.
  • Figure 5: Comparison of observed (blue), modeled (orange) and HURRI-GAN-corrected modeled (green) for: (a) Hermine (2016, category H1), (b) Harvey (2017, category H4), and (c) Ian (2022, category H5). The left side displays the location of two testing gauge stations (marked by colored disks) and the hurricane path (in red); while the right side shows the evaluation of the regression performance for these gauge stations. Evaluation statistics in each plot represent the performance of regression between modeled and observed water levels (without AI) and HURRI-GAN bias corrected and observed water levels (with AI).
  • ...and 1 more figures