Информация о публикации

Просмотр записей
Инд. авторы: Klimova E.G.
Заглавие: Methods of Spatial Data Processing Based on Bayesian Approach for Environmental Monitoring
Библ. ссылка: Klimova E.G. Methods of Spatial Data Processing Based on Bayesian Approach for Environmental Monitoring // CEUR Workshop Proceedings. - 2020. - Vol.2534. - P.118-123. - ISSN 1613-0073. - http://ceur-ws.org/Vol-2534/20_short_paper.pdf
Внешние системы: SCOPUS: 2-s2.0-85078527006;
Реферат: eng: One of the important tasks of monitoring of environment is the problem of obtaining the values of environmental parameters on a regular grid. At present, such problems are solved using all available observational data as well as the mathematical model of the process of interest to us. The mathematical formulation of the problem is included in the set of tasks of the so-called inverse modelling. If the probabilistic formulation of the problem is considered, the Bayesian approach is applied. This approach is used in popular algorithms, such as the ensemble Kalman filter, the ensemble Kalman smoothing, the particle method. The report provides a brief overview of modern methods, as well as approaches to their practical implementation. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Ключевые слова: Spatial data processing; Satellite data; Probabilistic formulation; Mathematical formulation; Environmental parameter; Environmental Monitoring; Ensemble Kalman Filter; Inverse problems; Monitoring; Kalman filters; Data handling; Bayesian networks; Satellite data; Ensemble Kalman filter; Data assimilation; Data assimilation;
Издано: 2020
Физ. характеристика: с.118-123
Ссылка: http://ceur-ws.org/Vol-2534/20_short_paper.pdf
Конференция: Название: Всероссийская конференция с международным участием «Обработка пространственных данных в задачах мониторинга природных и антропогенных процессов»
Аббревиатура: SDM-2019
Город: Бердск, Новосибирская область
Страна: Россия
Даты проведения: 2019-08-26 - 2019-08-30
Ссылка: http://conf.nsc.ru/SDM-2019