• Corpus ID: 208617482

Predicting Lake Erie Wave Heights using XGBoost

@article{Hu2019PredictingLE,
  title={Predicting Lake Erie Wave Heights using XGBoost},
  author={Haoguo Hu and Philip Chu},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.01786}
}
Dangerous large wave put the coastal communities and vessels operating under threats and wave predictions are strongly needed for early warnings. While numerical wave models, such as WAVEWATCH III (WW3), are useful to provide spatially continuous information to supplement in situ observations, however, they often require intensive computational costs. An attractive alternative is machine-learning method, which can potentially provide comparable performance of numerical wave models but only… 
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References

SHOWING 1-10 OF 11 REFERENCES

Application of two numerical models for wave hindcasting in Lake Erie

Wave climatology of Lake Erie based on an unstructured-grid wave model

Hindcast of wave dynamics in Lake Erie during 2002 to 2012 was conducted using a state-of-art finite-volume coastal ocean surface wave model (FVCOM-SWAVE). After model calibration, the surface

The Operational Implementation of a Great Lakes Wave Forecasting System at NOAA/NCEP*

AbstractThe development of a Great Lakes wave forecasting system at NOAA’s National Centers for Environmental Prediction (NCEP) is described. The system is an implementation of the WAVEWATCH III

Application of a Simple Numerical Wave Prediction Model

A parametric dynamical wave prediction model has been adapted and tested against semianalytic empirical results for steady conditions in a circular basin and extensive fiel measurements of wave

Cycle III version 40.51 technical documentation

  • P.O. Box
  • 2006

extension by neural networks and reanalysis wind data. Ocean Modelling, Volume 94, Pages 128-140

  • Ocean Model 28:153–166
  • 2009

XGBoost: A Scalable Tree Boosting System

This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.