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Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks

Author

Listed:
  • Boris V. Malozyomov

    (Department of Electrotechnical Complexes, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Nikita V. Martyushev

    (Department of Materials Science, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Svetlana N. Sorokova

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Egor A. Efremenkov

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Denis V. Valuev

    (Yurga Technological Institute (Branch), Tomsk Polytechnic University, 652055 Yurga, Russia)

  • Mengxu Qi

    (Department of Materials Science, Tomsk Polytechnic University, 634050 Tomsk, Russia)

Abstract

In this paper, we investigate mathematical models of meteorological forecasting based on the work of neural networks, which allow us to calculate presumptive meteorological parameters of the desired location on the basis of previous meteorological data. A new method of grouping neural networks to obtain a more accurate output result is proposed. An algorithm is presented, based on which the most accurate meteorological forecast was obtained based on the results of the study. This algorithm can be used in a wide range of situations, such as obtaining data for the operation of equipment in a given location and studying meteorological parameters of the location. To build this model, we used data obtained from personal weather stations of the Weather Underground company and the US National Digital Forecast Database (NDFD). Also, a Google remote learning machine was used to compare the results with existing products on the market. The algorithm for building the forecast model covered several locations across the US in order to compare its performance in different weather zones. Different methods of training the machine to produce the most effective weather forecast result were also considered.

Suggested Citation

  • Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks," Mathematics, MDPI, vol. 12(3), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:480-:d:1332275
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    References listed on IDEAS

    as
    1. Boris V. Malozyomov & Nikita V. Martyushev & Elena V. Voitovich & Roman V. Kononenko & Vladimir Yu. Konyukhov & Vadim Tynchenko & Viktor Alekseevich Kukartsev & Yadviga Aleksandrovna Tynchenko, 2023. "Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment," Energies, MDPI, vol. 16(13), pages 1-30, June.
    2. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    3. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    4. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    5. Boris V. Malozyomov & Vladimir Ivanovich Golik & Vladimir Brigida & Vladislav V. Kukartsev & Yadviga A. Tynchenko & Andrey A. Boyko & Sergey V. Tynchenko, 2023. "Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors," Energies, MDPI, vol. 16(11), pages 1-16, May.
    6. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
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