Methodological Issues of Using the Randomized Machine Learning for Forecasting the Dynamics of Thermokarst Arctic Lakes

• Published in: Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics
• Main Authors: Попков, Юрий Соломонович, Волкович, Зеев, Мельников, Андрей Витальевич, Полищук, Юрий Михайлович
• Format: Article in Journal/Newspaper
• Language: Russian
• Published: South Ural State University 2019
• Subjects: machine learning; entropy-randomized approach; randomized model; forecasting; spatio-temporal dynamics; permafrost; thermokarst lakes; satellite images; meteorological data reanalysis; climate change; global warming; машинное обучение; энтропийно-рандомизированный подход; рандомизированная модель; прогнозирование; пространственно-временная динамика; вечная мерзлота; термокарстовые озера; спутниковые снимки; реанализ метеоданных; климатические изменения; Aphrodite; Arctic; Climate change; Global warming; Thermokarst; термокарст*; Siberia
• Online Access: https://vestnik.susu.ru/ctcr/article/view/9174; https://doi.org/10.14529/ctcr190401

The analysis of the state of the problems of modeling the spatio-temporal dynamics of the lake fields under the conditions of modern climate changes is carried out. It is shown that the analytical methods used in studying the dynamics of thermokarst processes in individual lakes are not suitable for...