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

Bibliographic Details
Parent link:Mathematics.— .— Basel: MDPI AG
Vol. 12, iss. 3.— 2024.— Article number 480, 17 p.
Corporate Author: National Research Tomsk Polytechnic University
Other Authors: Malozyomov B. V. Boris Vitaljevich, Martyushev N. V. Nikita Vladimirovich, Sorokova S. N. Svetlana Nikolaevna, Efremenkov (Ephremenkov) E. A. Egor Alekseevich, Valuev D. V. Denis Viktorovich, Qi Mengxu
Summary:Title screen
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.
Текстовый файл
Published: 2024
Subjects:
Online Access:http://earchive.tpu.ru/handle/11683/132480
https://doi.org/10.3390/math12030480
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=672142

MARC

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330 |a 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. 
336 |a Текстовый файл 
461 1 |c Basel  |n MDPI AG  |t Mathematics 
463 1 |d 2024  |t Vol. 12, iss. 3  |v Article number 480, 17 p. 
610 1 |a электронный ресурс 
610 1 |a труды учёных ТПУ 
610 1 |a weather mathematical model 
610 1 |a forecast 
610 1 |a neural network 
610 1 |a algorithm for building weather forecasts 
701 1 |a Malozyomov  |b B. V.  |g Boris Vitaljevich 
701 1 |a Martyushev  |b N. V.  |c specialist in the field of material science  |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences  |f 1981-  |g Nikita Vladimirovich  |9 16754 
701 1 |a Sorokova  |b S. N.  |c specialist in the field of Informatics and computer engineering  |c associate Professor of Tomsk Polytechnic University, programmer, candidate of physico-mathematical Sciences  |f 1981-  |g Svetlana Nikolaevna  |9 16596 
701 1 |a Efremenkov (Ephremenkov)  |b E. A.  |c Specialist in the field of mechanical engineering  |c Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences (PhD)  |f 1975-  |g Egor Alekseevich  |9 14780 
701 1 |a Valuev  |b D. V.  |c specialist in the field of metal working  |c Associate Professor of Yurga technological Institute of Tomsk Polytechnic University, Candidate of technical sciences  |f 1980-  |g Denis Viktorovich  |9 16748 
701 0 |a Qi Mengxu 
712 0 2 |a National Research Tomsk Polytechnic University  |c (2009- )  |9 27197 
801 0 |a RU  |b 63413507  |c 20240409 
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