Rainfall rate prediction based on artificial neural networks for rain fade mitigation over earth-satellite link

Mary N. Ahuna, Thomas J. Afullo, Akintunde A. Alonge

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)


In this paper, we present a model for rainfall rate prediction 30 seconds ahead of time using an artificial neural network. The resultant predicted rainfall rate can then be used in determining an appropriate fade counter-measure, for instance, digital modulation scheme ahead of time, to keep the bit error rate (BER) on the link within acceptable levels to allow constant flow of data on the link during a rain event. The approach used in this paper is pattern recognition technique that considers historical rainfall rate patterns over Durban (29.8587°S, 31.0218°E). The resultant prediction model is found to predict an immediate future rain rate when given three adjacent historical rain rates. For our model validation, error analysis via root mean square (RMSE) technique on our prediction model results show that resultant errors lie within acceptable values at different rain events within different rainfall regimes.

Original languageEnglish
Title of host publication2017 IEEE AFRICON
Subtitle of host publicationScience, Technology and Innovation for Africa, AFRICON 2017
EditorsDarryn R. Cornish
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538627754
Publication statusPublished - 3 Nov 2017
Externally publishedYes
EventIEEE AFRICON 2017 - Cape Town, South Africa
Duration: 18 Sept 201720 Sept 2017

Publication series

Name2017 IEEE AFRICON: Science, Technology and Innovation for Africa, AFRICON 2017


ConferenceIEEE AFRICON 2017
Country/TerritorySouth Africa
CityCape Town


  • Backpropagation neural network
  • Rain event
  • Rainfall rate
  • Rainfall rate prediction

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


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