title: Theoretical foundations of probabilistic and statistical forecasting of agrometeorological risks
metadata.name: dc.contributor.author
metadata.value: Iakushev, Viktor Р.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrofanova, Olga А.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrofanov, Evgenii Р.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2021-07-14T13:53:05Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2021-07-14T13:53:05Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Iakushev V. Р., Bure V. M., Mitrofanova О. А., Mitrofanov Е. Р. Theoretical foundations of probabilistic and statistical forecasting of agrometeorological risks. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2021, vol. 17, iss. 2, pp. 174-182.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https //doi.org/10.21638/11701/spbu10.2021.207
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/29869
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Еach 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.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This work is supported by the Russian Federation (agreement with the Ministry of Science and Education) (project N 075-15-2020-805 dated October 02, 2020).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 17; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: one-dimensional time series
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: forecasting
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: droughts
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: frosts
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: agrometeorological hazards
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: intelligent system
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Theoretical foundations of probabilistic and statistical forecasting of agrometeorological risks
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 174-182.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/29869/1/174-182.pdf
resources.format: Adobe PDF
resources.size: 422,96 kB