References

This page collects the main dataset and model references cited throughout the PyHazards docs. It is a project reference list, not an exhaustive bibliography.

Dataset References

  • Gelaro, R., McCarty, W., Suárez, M. J., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). [link].

  • Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. [link].

  • NOAA National Centers for Environmental Information (NCEI). Storm Events Database Documentation. [link].

  • Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. (2014). The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. [link].

  • Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z., Quayle, B., and Howard, S. (2007). A project for monitoring trends in burn severity. [link].

  • Rollins, M. G. (2009). LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel assessment. [link].

  • National Interagency Fire Center (NIFC). Wildland Fire Incident Geospatial Services (WFIGS). [link].

  • Schmit, T. J., Griffith, P., Gunshor, M. M., et al. (2017). A closer look at the ABI on the GOES-R series. [link].

Model References

Wildfire

  • Developing risk assessment framework for wildfire in the United States. [paper].

  • Application of Explainable Artificial Intelligence in Predicting Wildfire Spread: An ASPP-Enabled CNN Approach. [paper].

  • Wildfire Danger Prediction and Understanding with Deep Learning. [paper], [repo].

  • WildfireSpreadTS: A Dataset of Multi-Modal Time Series for Wildfire Spread Prediction. [paper], [repo].

  • Wildfire Spread Prediction in North America Using Satellite Imagery and Vision Transformer. [paper], [repo].

  • ForeFire: A Modular, Scriptable C++ Simulation Engine and Library for Wildland-Fire Spread. [paper], [repo].

  • Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011. [paper], [repo].

  • FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction. [paper], [repo].

Earthquake

  • Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning. [paper].

  • PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. [paper], [repo].

  • Earthquake Transformer-An attentive deep-learning model for simultaneous earthquake detection and phase picking. [paper], [repo].

  • Generalized Seismic Phase Detection with Deep Learning. [paper], [repo].

  • An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning. [paper], [repo].

Flood

  • Interpretable physics-informed graph neural networks for flood forecasting. [paper].

  • Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. [paper], [repo].

  • Large-scale flood modeling and forecasting with FloodCast. [paper], [repo].

  • UrbanFloodCast: WMO Urban Flooding Forecasting Challenge. [paper], [repo].

  • Global Flood Forecasting at a Fine Catchment Resolution using Machine Learning. [paper], [repo].

Hurricane and Tropical Cyclone

  • Hurricane Forecasting: A Novel Multimodal Machine Learning Framework. [paper], [repo].

  • Deep Learning Experiments for Tropical Cyclone Intensity Forecasts. [paper], [repo].

  • Benchmark dataset and deep learning method for global tropical cyclone forecasting. [paper], [repo].

  • SAF-Net: A spatio-temporal deep learning method for typhoon intensity prediction. [paper], [repo].

  • Tropical cyclone intensity forecasting using model knowledge guided deep learning model. [paper], [repo].

  • GraphCast: Learning skillful medium-range global weather forecasting. [paper], [repo].

  • Accurate medium-range global weather forecasting with 3D neural networks. [paper], [repo].

  • FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. [paper], [repo].

Benchmark and Data Resources