TY - JOUR

T1 - Теоретические основы вероятностно-статистического прогнозирования неблагоприятных агрометеоусловий

AU - Iakushev, V. P.

AU - Bure, V. M.

AU - Mitrofanova, O. A.

AU - Mitrofanov, E. P.

N1 - Publisher Copyright: © 2021 Saint Petersburg State University. All rights reserved.

PY - 2021

Y1 - 2021

N2 - Each model for forecasting agrometeorological risks based on the analysis of one-dimensional time series is effective for a certain range of initial information. In addition, the values of the initial observations can differ significantly for each specific case, respectively, the widespread use of one method for the analysis of arbitrary information can lead to significant inaccuracies. Thus, the problem of choosing a forecasting method for the initial set of agrometeorological data arises. In this regard, a universal adaptive probabilistic-statistical approach to predicting agrometeorological risks is proposed, which makes it possible to solve the problem of choosing a model. The article presents the results of the first stage of research carried out with the financial support of the Ministry of Education and Science of the Russian Federation: a brief overview of the current state of research in this direction is presented, theoretical foundations for predicting agrometeorological risks for a possible onset of drought and frost have been developed, including the task of generating initial information, a description of basic forecasting models, and also a direct description of the proposed approach with a presentation of the general structure of an intelligent system, on the basis of which the corresponding algorithm can be developed and automated as directions for further work.

AB - Each model for forecasting agrometeorological risks based on the analysis of one-dimensional time series is effective for a certain range of initial information. In addition, the values of the initial observations can differ significantly for each specific case, respectively, the widespread use of one method for the analysis of arbitrary information can lead to significant inaccuracies. Thus, the problem of choosing a forecasting method for the initial set of agrometeorological data arises. In this regard, a universal adaptive probabilistic-statistical approach to predicting agrometeorological risks is proposed, which makes it possible to solve the problem of choosing a model. The article presents the results of the first stage of research carried out with the financial support of the Ministry of Education and Science of the Russian Federation: a brief overview of the current state of research in this direction is presented, theoretical foundations for predicting agrometeorological risks for a possible onset of drought and frost have been developed, including the task of generating initial information, a description of basic forecasting models, and also a direct description of the proposed approach with a presentation of the general structure of an intelligent system, on the basis of which the corresponding algorithm can be developed and automated as directions for further work.

KW - Agrometeorological hazards

KW - Droughts

KW - Forecasting

KW - Frosts

KW - Intelligent system

KW - One-dimensional time series

KW - droughts

KW - forecasting

KW - frosts

KW - agrometeorological hazards

KW - intelligent system

KW - one-dimensional time series

UR - http://www.scopus.com/inward/record.url?scp=85111971328&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b1a7c467-e8dc-3f41-a98a-571099ae1e53/

U2 - 10.21638/11701/SPBU10.2021.207

DO - 10.21638/11701/SPBU10.2021.207

M3 - статья

AN - SCOPUS:85111971328

VL - 17

SP - 174

EP - 182

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

SN - 1811-9905

IS - 2

ER -