Standard
Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena. / Stankova, Elena N.; Grechko, Irina A.; Kachalkina, Yana N.; Khvatkov, Evgeny V.
Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017. ed. / Ana Maria A.C. Rocha; Elena Stankova; Sanjay Misra; Giuseppe Borruso; Alfredo Cuzzocrea; David Taniar; Osvaldo Gervasi; Beniamino Murgante; Carmelo M. Torre; Bernady O. Apduhan. Springer Nature, 2017. p. 495-504 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10408 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Stankova, EN, Grechko, IA, Kachalkina, YN & Khvatkov, EV 2017,
Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena. in AMAC Rocha, E Stankova, S Misra, G Borruso, A Cuzzocrea, D Taniar, O Gervasi, B Murgante, CM Torre & BO Apduhan (eds),
Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10408 LNCS, Springer Nature, pp. 495-504, 17th International Conference on Computational Science and Its Applications, ICCSA 2017, Trieste, Italy,
2/07/17.
https://doi.org/10.1007/978-3-319-62404-4_37
APA
Stankova, E. N., Grechko, I. A., Kachalkina, Y. N., & Khvatkov, E. V. (2017).
Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena. In A. M. A. C. Rocha, E. Stankova, S. Misra, G. Borruso, A. Cuzzocrea, D. Taniar, O. Gervasi, B. Murgante, C. M. Torre, & B. O. Apduhan (Eds.),
Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017 (pp. 495-504). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10408 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-319-62404-4_37
Vancouver
Stankova EN, Grechko IA, Kachalkina YN, Khvatkov EV.
Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena. In Rocha AMAC, Stankova E, Misra S, Borruso G, Cuzzocrea A, Taniar D, Gervasi O, Murgante B, Torre CM, Apduhan BO, editors, Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017. Springer Nature. 2017. p. 495-504. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-319-62404-4_37
Author
BibTeX
@inproceedings{eefad7f817e541deafc07de96c5746d4,
title = "Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena",
abstract = "The paper is a continuation of the works [1–4] where has been shown how the technologies of machine learning and online analytical processing (OLAP) could be used in conjunction with the numerical model of convective cloud for forecasting dangerous convective phenomena such as thunderstorm, heavy rainfall and hail. We study specifically the possibility of making predictions via a hybrid approach that combines the predictive numerical model of convective cloud with the modern methods of big data processing. We overview the existing examples of using of machine learning tools for weather forecasting and discuss the range of their applicability.",
keywords = "Data mining, Machine learning, Multidimensional data base, Numerical model of convective cloud, OLAP, Online analytical processing, Thunderstorm, Validation of numerical models, Weather forecasting",
author = "Stankova, {Elena N.} and Grechko, {Irina A.} and Kachalkina, {Yana N.} and Khvatkov, {Evgeny V.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 17th International Conference on Computational Science and Its Applications, ICCSA 2017 ; Conference date: 02-07-2017 Through 05-07-2017",
year = "2017",
doi = "10.1007/978-3-319-62404-4_37",
language = "English",
isbn = "9783319624037",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "495--504",
editor = "Rocha, {Ana Maria A.C.} and Elena Stankova and Sanjay Misra and Giuseppe Borruso and Alfredo Cuzzocrea and David Taniar and Osvaldo Gervasi and Beniamino Murgante and Torre, {Carmelo M.} and Apduhan, {Bernady O.}",
booktitle = "Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017",
address = "Germany",
}
RIS
TY - GEN
T1 - Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena
AU - Stankova, Elena N.
AU - Grechko, Irina A.
AU - Kachalkina, Yana N.
AU - Khvatkov, Evgeny V.
N1 - Conference code: 17
PY - 2017
Y1 - 2017
N2 - The paper is a continuation of the works [1–4] where has been shown how the technologies of machine learning and online analytical processing (OLAP) could be used in conjunction with the numerical model of convective cloud for forecasting dangerous convective phenomena such as thunderstorm, heavy rainfall and hail. We study specifically the possibility of making predictions via a hybrid approach that combines the predictive numerical model of convective cloud with the modern methods of big data processing. We overview the existing examples of using of machine learning tools for weather forecasting and discuss the range of their applicability.
AB - The paper is a continuation of the works [1–4] where has been shown how the technologies of machine learning and online analytical processing (OLAP) could be used in conjunction with the numerical model of convective cloud for forecasting dangerous convective phenomena such as thunderstorm, heavy rainfall and hail. We study specifically the possibility of making predictions via a hybrid approach that combines the predictive numerical model of convective cloud with the modern methods of big data processing. We overview the existing examples of using of machine learning tools for weather forecasting and discuss the range of their applicability.
KW - Data mining
KW - Machine learning
KW - Multidimensional data base
KW - Numerical model of convective cloud
KW - OLAP
KW - Online analytical processing
KW - Thunderstorm
KW - Validation of numerical models
KW - Weather forecasting
UR - http://www.scopus.com/inward/record.url?scp=85026757452&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-62404-4_37
DO - 10.1007/978-3-319-62404-4_37
M3 - Conference contribution
AN - SCOPUS:85026757452
SN - 9783319624037
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 495
EP - 504
BT - Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017
A2 - Rocha, Ana Maria A.C.
A2 - Stankova, Elena
A2 - Misra, Sanjay
A2 - Borruso, Giuseppe
A2 - Cuzzocrea, Alfredo
A2 - Taniar, David
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Torre, Carmelo M.
A2 - Apduhan, Bernady O.
PB - Springer Nature
T2 - 17th International Conference on Computational Science and Its Applications, ICCSA 2017
Y2 - 2 July 2017 through 5 July 2017
ER -
