OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds

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OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds. / Stankova, Elena N. ; Balakshiy, Andrey V. ; Petrov, Dmitry A. ; Korkhov , Vladimir V. ; Shorov , Andrey V. .

In: International Journal of Business Intelligence and Data Mining, Vol. 14, No. 1/2, 2019, p. 254-266.

Research output: Contribution to journal › Article › peer-review

Author

Stankova, Elena N. ; Balakshiy, Andrey V. ; Petrov, Dmitry A. ; Korkhov , Vladimir V. ; Shorov , Andrey V. . / OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds. In: International Journal of Business Intelligence and Data Mining. 2019 ; Vol. 14, No. 1/2. pp. 254-266.

BibTeX

@article{fa4d6b3b5791485aa67bbe4df96ab09f,

title = "OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds",

abstract = "In the present work we use the technologies of machine learning and OLAP for more accurate forecasting of such phenomena as a thunderstorm, hail, heavy rain, using the numerical model of convective cloud. Three methods of machine learning: support vector machine, logistic regression and ridge regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain). Previously developed complex information system is used for collecting the data about the state of the atmosphere and about the place and at the time when dangerous convective phenomena are recorded.",

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 Balakshiy, {Andrey V.} and Petrov, {Dmitry A.} and Korkhov, {Vladimir V.} and Shorov, {Andrey V.}",

note = "Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Korkhov, V.V. and Shorov, A.V. (2019) {\textquoteleft}OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds{\textquoteright}, Int. J. Business Intelligence and Data Mining, Vol. 14, Nos. 1/2, pp.254–266. ",

year = "2019",

doi = "10.1504/IJBIDM.2019.096793",

language = "English",

volume = "14",

pages = "254--266",

journal = "International Journal of Business Intelligence and Data Mining",

issn = "1743-8187",

publisher = "Inderscience",

number = "1/2",

}

RIS

TY - JOUR

T1 - OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds

AU - Stankova, Elena N.

AU - Balakshiy, Andrey V.

AU - Petrov, Dmitry A.

AU - Korkhov , Vladimir V.

AU - Shorov , Andrey V.

N1 - Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Korkhov, V.V. and Shorov, A.V. (2019) ‘OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds’, Int. J. Business Intelligence and Data Mining, Vol. 14, Nos. 1/2, pp.254–266.

PY - 2019

Y1 - 2019

N2 - In the present work we use the technologies of machine learning and OLAP for more accurate forecasting of such phenomena as a thunderstorm, hail, heavy rain, using the numerical model of convective cloud. Three methods of machine learning: support vector machine, logistic regression and ridge regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain). Previously developed complex information system is used for collecting the data about the state of the atmosphere and about the place and at the time when dangerous convective phenomena are recorded.

AB - In the present work we use the technologies of machine learning and OLAP for more accurate forecasting of such phenomena as a thunderstorm, hail, heavy rain, using the numerical model of convective cloud. Three methods of machine learning: support vector machine, logistic regression and ridge regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain). Previously developed complex information system is used for collecting the data about the state of the atmosphere and about the place and at the time when dangerous convective phenomena are recorded.

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=85058816277&partnerID=8YFLogxK

U2 - 10.1504/IJBIDM.2019.096793

DO - 10.1504/IJBIDM.2019.096793

M3 - Article

VL - 14

SP - 254

EP - 266

JO - International Journal of Business Intelligence and Data Mining

JF - International Journal of Business Intelligence and Data Mining

SN - 1743-8187

IS - 1/2

ER -

ID: 37246919