By Topic

Snowfall and rainfall forecasting from the images of weather radar with artificial neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Ochiai, K. ; NTT HI Labs., Kanagawa, Japan ; Suzuki, H. ; Suzuki, S. ; Sonehara, N.
more authors

We discuss problems of the weather forecasting technique with artificial neural networks and describe some solutions. We show that the computational time for learning with an acceleration learning algorithm can be reduced about 10 percent. To overcome the problem of overtraining, the pruning method is introduced and the prediction error is decreased about 20 percent. Using the data obtained over a winter, the prediction error with the neural technique is reduced about 60 percent than that with the cross correlation method

Published in:

Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop

Date of Conference:

4-6 Sep 1996