Skip to main content

A Neural Network Model for Predicting Atlantic Hurricane Activity

  • Chapter
Interfaces in Computer Science and Operations Research

Part of the book series: Operations Research/Computer Science Interfaces Series ((ORCS,volume 7))

  • 287 Accesses

Abstract

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feed forward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. W. M. Gray, “Forecast of Atlantic Seasonal Hurricane Activity for 1994,” Technical Report, Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado (1994).

    Google Scholar 

  2. W. M. Gray, C. W. Landsea, P. W. Mielke, and K. J. Berry, “Predicting Atlantic Seasonal Hurricane Activity 6–11 Months in Advance,” Weather and Forecasting, 7, 440–455 (1992).

    Article  Google Scholar 

  3. W. M. Gray, C. W. Landsea, P. W. Mielke, and K. J. Berry, “Predicting Atlantic Basin Seasonal Tropical Cyclone Activity by 1 August,” Weather and Forecasting, 8, 73–86 (1993).

    Article  Google Scholar 

  4. W. M. Gray, C. W. Landsea, P. W. Mielke, and K. J. Berry, “Predicting Atlantic Basin Seasonal Tropical Cyclone Activity by 1 June,” Weather and Forecasting, 9, 103–115 (1994).

    Article  Google Scholar 

  5. O. Kwon, B. Golden, E. Wasil, and R. Gordon, “Modeling Hurricane Activity Using Neural Networks,” in Proceedings of the International Conference on Neural Information Processing Volume 2 (edited by M-W. Kim and S-Y. Lee), Seoul, Korea, 1005–1009, (1994).

    Google Scholar 

  6. R. H. Simpson, “The Hurricane Disaster-Potential Scale,” Weatherwise, 27, 169 and 186 (1974).

    Google Scholar 

  7. W. K. Stevens, “95 Hurricane Season Seen as One of the Fiercest in the Last 20 Years,” The New York Times, Vol. CXLIV, No. 50105 (June 27), C1 and C7 (1995).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this chapter

Cite this chapter

Kwon, O., Golden, B., Wasil, E. (1997). A Neural Network Model for Predicting Atlantic Hurricane Activity. In: Barr, R.S., Helgason, R.V., Kennington, J.L. (eds) Interfaces in Computer Science and Operations Research. Operations Research/Computer Science Interfaces Series, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4102-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-4102-8_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6837-3

  • Online ISBN: 978-1-4615-4102-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics