Machine Learning Techniques for Evaluating Disdrometer-Derived Raindrop Measurements Over Radio Links

Tsietsi Ramatladi, Akintunde Alonge

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


Rainfall drop size distribution (DSD) is an essential parameter for designing and operating radio links as found in numerous terrestrial and satellite systems. Utilizing machine learning techniques to analyze the number of raindrops within specific diameter bins can enhance DSD computations and provide valuable insights into other rainfall characteristics. This study investigates three regression models - decision tree regression (DTR), random forest regression (RFR), and k-nearest neighbours regression (KNNR) - as tools for evaluating raindrop measurements from the Joss-Waldvogel disdrometer (JWD). High-resolution rainfall measurements obtained from Durban, South Africa, are used to train and test regression models. The three proposed techniques accurately predicted raindrop presence across designated channels. However, the KNNR model performs better compared to other models, for all categories of investigated rainfall regimes. This research demonstrates the effectiveness of machine learning (ML) models in predicting and replicating disdrometer measurements over radio links. The findings in this study can serve as valuable input for radio link design over sub-tropical areas like Durban, where rainfall is highly variable.

Original languageEnglish
Title of host publicationProceedings of the 16th IEEE AFRICON, AFRICON 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350336214
Publication statusPublished - 2023
Event16th IEEE AFRICON, AFRICON 2023 - Nairobi, Kenya
Duration: 20 Sept 202322 Sept 2023

Publication series

NameIEEE AFRICON Conference
ISSN (Print)2153-0025
ISSN (Electronic)2153-0033


Conference16th IEEE AFRICON, AFRICON 2023


  • Machine learning
  • decision tree regression (DTR)
  • k-Nearest Neighbors regression (KNNR)
  • raindrop size distribution (DSD)
  • random forest regression (RFR)

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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