Using Technologies of OLAP and Machine Learning for Validation of the Numerical Models of Convective Clouds
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- 关键词：Numerical modeling ; Weather forecasting ; Machine learning ; OLAP technology ; Multidimensional data base
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9788
- 期：1
- 页码：463-472
- 全文大小：1,592 KB
- 参考文献：1.Petrov, D.A., Stankova, E.N.: Use of consolidation technology for meteorological data processing. In: Murgante, B., et al. (eds.) ICCSA 2014, Part I. LNCS, vol. 8579, pp. 440–451. Springer, Heidelberg (2014)
2.Petrov, D.A., Stankova, E.N.: Integrated information system for verification of the models of convective clouds. In: Gervasi, O., et al. (eds.) ICCSA 2015. LNCS, vol. 9158, pp. 321–330. Springer, Heidelberg (2015). doi:10.1007/978-3-319-21410-8_25 CrossRef
3.Stankova, E.N., Petrov, D.A.: Complex information system for organization of the input data of models of convective clouds. Appl. Math. Comput. Sci. Cont. Process. (3), 83–95 (2015) (in Russian). Vestnik of Saint-Petersburg University Series 10
4.Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, Heidelberg (2009). http://statweb.stanford.edu/~tibs/ElemStatLearn/
5.Mitchell, T.: Machine Learning. Springer, Heidelberg (2009)
6.Raba, N.O. Stankova, E.N.: Research of influence of compensating descending flow on cloud’s life cycle by means of 1.5-dimensional model with 2 cylinders. In: Proceedings of MGO, vol. 559, pp. 192–209 (2009) (in Russian)
7.Raba, N.O., Stankova, E.N., Ampilova, N.: On investigation of parallelization effectiveness with the help of multi-core processors. Procedia Comput. Sci. 1(1), 2757–2762 (2010)CrossRef
8.Raba, N., Stankova, E.: On the possibilities of multi-core processor use for real-time forecast of dangerous convective phenomena. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010, Part II. LNCS, vol. 6017, pp. 130–138. Springer, Heidelberg (2010). ISBN: 978-3-642-12164-7CrossRef
9.Raba, N.O., Stankova, E.N.: On the problem of numerical modeling of dangerous convective phenomena: possibilities of real-time forecast with the help of multi-core processors. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part V. LNCS, vol. 6786, pp. 633–642. Springer, Heidelberg (2011). ISSN: 0302-9743CrossRef
10.Raba, N.O., Stankova, E.N.: On the effectiveness of using the GPU for numerical solution of stochastic collection equation. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part V. LNCS, vol. 7975, pp. 248–258. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39640-3_18 CrossRef
11.Date, C.J., Darwen, H.: Foundation for Future Database Systems: The Third Manifesto, 2nd edn., pp. 223–238. Addison Wesley Professional, Reading (2000)
12.Codd, E.F.: Providing OLAP (on-line analytical processing) to user-analysts: an IT mandate. Technical Report, E.F. Codd and Associates (1993)
13.Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multi-dimensional databases. IBM Research Report, IBM Almaden Research Center, September 1995
14.Gyssens, M., Lakshmanan, L.V.S.: A foundation for multi-dimensional databases. Technical Report, Concordia University and University of Limburg, February 1997
15.Matveev, L.T.: Physics of Atmosphere. Saint-Petersburg, Hidrometeoizdat, p. 779 (in Russian)
16.Jedox. www.jedox.com/ - 作者单位：Elena N. Stankova (22) (23)
Andrey V. Balakshiy (22)
Dmitry A. Petrov (22)
Andrey V. Shorov (23)
Vladimir V. Korkhov (22)
22. Saint Petersburg State University, 7-9, Universitetskaya nab., St. Petersburg, 199034, Russia
23. Saint Petersburg Electrotechnical University “LETI”, (SPbETU), ul. Professora Popova 5, St. Petersburg, 197376, Russia - 丛书名：Computational Science and Its Applications -- ICCSA 2016
- ISBN：978-3-319-42111-7
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
- 卷排序：9788
文摘
The paper is a continuation of the works [1–3] where complex information system for organization of the input data for the models of convective clouds is presented. In the present work we use the information system for obtaining statistically significant amount of meteorological data about the state of the atmosphere in the place and at the time when dangerous convective phenomena are recorded. Corresponding amount of information has been collected about the state of the atmosphere in cases when no dangerous convective phenomena have been observed. Feature selection for thunderstorm forecasting based on Recursive feature elimination with cross-validation algorithm is provided. 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).