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

Boris V. Malozyomov, Nikita V. Martyushev (), Svetlana N. Sorokova, Egor A. Efremenkov, Denis V. Valuev and Mengxu Qi
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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

Mathematics, 2024, vol. 12, issue 3, 1-17

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.

Keywords: weather mathematical model; forecast; neural network; algorithm for building weather forecasts (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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