DOI

The authors propose a boosted k-nearest neighbour algorithm for numerical forecasting of dangerous convective phenomena. The algorithm is applied for processing the output data of the numerical cloud model. The results show that boosted algorithm is able to predict the very fact of convective phenomena occurrence with the high accuracy, but it is not so good in distinguishing the specific type of convective phenomena. Comparison with the k-NN algorithm without boosting shows that boosting resulted in better accuracy of forecasting.

Original languageEnglish
Title of host publicationComputational Science and Its Applications- ICCSA 2019 - 19th International Conference, Proceedings
Subtitle of host publication19th International Conference Saint Petersburg, Russia, July 1–4, 2019 Proceedings, Part IV
EditorsSanjay Misra, Osvaldo Gervasi, Beniamino Murgante, Elena Stankova, Vladimir Korkhov, Carmelo Torre, Eufemia Tarantino, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan
PublisherSpringer Nature
Pages802–811
Number of pages10
Volume11622
ISBN (Electronic)1611-3349
ISBN (Print)9783030243043
DOIs
StatePublished - 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11622 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

    Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

    Research areas

  • AdaBoost algorithm, Boosted k-nearest neighbour algorithm, Machine learning, Naive bayes classifier, Numerical model of convective cloud, Support-vector machine model, Thunderstorm forecasting, Weather forecasting

ID: 43545758