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On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena. / Stankova, E. N.; Dyachenko, N. V.; Tokareva, I.O.

Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. ред. / Osvaldo Gervasi; Beniamino Murgante; Sanjay Misra; Chiara Garau; Ivan Blecic; David Taniar; Bernady O. Apduhan; Ana Maria A.C. Rocha; Eufemia Tarantino; Carmelo Maria Torre; Yeliz Karaca. Springer Nature, 2020. стр. 82-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12254 LNCS).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Stankova, EN, Dyachenko, NV & Tokareva, IO 2020, On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena. в O Gervasi, B Murgante, S Misra, C Garau, I Blecic, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino, CM Torre & Y Karaca (ред.), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 12254 LNCS, Springer Nature, стр. 82-93, 20th International Conference on Computational Science and Its Applications, ICCSA 2020, Cagliari, Италия, 1/07/20. https://doi.org/10.1007/978-3-030-58817-5_7

APA

Stankova, E. N., Dyachenko, N. V., & Tokareva, I. O. (2020). On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena. в O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blecic, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, C. M. Torre, & Y. Karaca (Ред.), Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings (стр. 82-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12254 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-58817-5_7

Vancouver

Stankova EN, Dyachenko NV, Tokareva IO. On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena. в Gervasi O, Murgante B, Misra S, Garau C, Blecic I, Taniar D, Apduhan BO, Rocha AMAC, Tarantino E, Torre CM, Karaca Y, Редакторы, Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Springer Nature. 2020. стр. 82-93. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-58817-5_7

Author

Stankova, E. N. ; Dyachenko, N. V. ; Tokareva, I.O. / On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena. Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings. Редактор / Osvaldo Gervasi ; Beniamino Murgante ; Sanjay Misra ; Chiara Garau ; Ivan Blecic ; David Taniar ; Bernady O. Apduhan ; Ana Maria A.C. Rocha ; Eufemia Tarantino ; Carmelo Maria Torre ; Yeliz Karaca. Springer Nature, 2020. стр. 82-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{ac5249f336d140119bb344a23b3f6c81,
title = "On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena",
abstract = "The paper considers the possibility of thunderstorm forecasting using only dynamical and microphysical parameters of the cloud, simulated by the 1.5D model with further processing by machine learning methods. The problem of feature selection is discussed in two aspects: selection of the optimal values of time and height when and where the output model data are fixed and selection of fixed set of the most representative cloud parameters (features) among all output cloud characteristics. Five machine learning methods are considered: Support Vector Machine (SVM), Logistic Regression, Ridge Regression, boosted k-nearest neighbour algorithm and neural networks. It is shown that forecast accuracy of all five methods reaches values exceeding 90%.",
keywords = "Boosted k-nearest neighbour algorithm, Logistic Regression, Machine learning, Neural networks, Numerical model of convective cloud, Ridge Regression, Support Vector Machine (SVM), Thunderstorm forecasting, Weather forecasting",
author = "Stankova, {E. N.} and Dyachenko, {N. V.} and I.O. Tokareva",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 20th International Conference on Computational Science and Its Applications, ICCSA 2020 ; Conference date: 01-07-2020 Through 04-07-2020",
year = "2020",
doi = "10.1007/978-3-030-58817-5_7",
language = "English",
isbn = "9783030588168",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "82--93",
editor = "Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Chiara Garau and Ivan Blecic and David Taniar and Apduhan, {Bernady O.} and Rocha, {Ana Maria A.C.} and Eufemia Tarantino and Torre, {Carmelo Maria} and Yeliz Karaca",
booktitle = "Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings",
address = "Germany",
url = "http://iccsa.org/",

}

RIS

TY - GEN

T1 - On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena

AU - Stankova, E. N.

AU - Dyachenko, N. V.

AU - Tokareva, I.O.

N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - The paper considers the possibility of thunderstorm forecasting using only dynamical and microphysical parameters of the cloud, simulated by the 1.5D model with further processing by machine learning methods. The problem of feature selection is discussed in two aspects: selection of the optimal values of time and height when and where the output model data are fixed and selection of fixed set of the most representative cloud parameters (features) among all output cloud characteristics. Five machine learning methods are considered: Support Vector Machine (SVM), Logistic Regression, Ridge Regression, boosted k-nearest neighbour algorithm and neural networks. It is shown that forecast accuracy of all five methods reaches values exceeding 90%.

AB - The paper considers the possibility of thunderstorm forecasting using only dynamical and microphysical parameters of the cloud, simulated by the 1.5D model with further processing by machine learning methods. The problem of feature selection is discussed in two aspects: selection of the optimal values of time and height when and where the output model data are fixed and selection of fixed set of the most representative cloud parameters (features) among all output cloud characteristics. Five machine learning methods are considered: Support Vector Machine (SVM), Logistic Regression, Ridge Regression, boosted k-nearest neighbour algorithm and neural networks. It is shown that forecast accuracy of all five methods reaches values exceeding 90%.

KW - Boosted k-nearest neighbour algorithm

KW - Logistic Regression

KW - Machine learning

KW - Neural networks

KW - Numerical model of convective cloud

KW - Ridge Regression

KW - Support Vector Machine (SVM)

KW - Thunderstorm forecasting

KW - Weather forecasting

UR - http://www.scopus.com/inward/record.url?scp=85092662800&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/43f3877f-0207-3184-a673-9b9f55ecf49c/

U2 - 10.1007/978-3-030-58817-5_7

DO - 10.1007/978-3-030-58817-5_7

M3 - Conference contribution

AN - SCOPUS:85092662800

SN - 9783030588168

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 82

EP - 93

BT - Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Misra, Sanjay

A2 - Garau, Chiara

A2 - Blecic, Ivan

A2 - Taniar, David

A2 - Apduhan, Bernady O.

A2 - Rocha, Ana Maria A.C.

A2 - Tarantino, Eufemia

A2 - Torre, Carmelo Maria

A2 - Karaca, Yeliz

PB - Springer Nature

T2 - 20th International Conference on Computational Science and Its Applications, ICCSA 2020

Y2 - 1 July 2020 through 4 July 2020

ER -

ID: 70311027