Work

Spatiotemporal Analysis of the Termokarst Lakes Evolution Using Remote Sensing and Randomized Machine Learning and Forecasting

Year: 2024

Type: article

Abstract: The work is devoted to the problem of forecasting the evolution of the area of thermokarst lakes in the Arctic permafrost zone using the analysis of test areas from several geographical regions. The approach proposed in the work is based on the Randomized Machine Learning method for constructing mathematical models of lake area from climate indicators, learning the model on real data and further forecasting. The results of modeling the dynamics of lake areas using linear static and dynamic models are presented and compared. It is shown that proposed dynamic model can significantly reduce the average modeling error

Source: Rossijskij žurnal nauk o zemle/Russian journal of earth sciences

DOI: 10.2205/2024es000935

Authors: Yuri Dubnov, Alexey Popkov, Yuri Popkov, Matvey Kuprianov, Vladimir Polyschuk, Andrey Melnikov, Yuri Polyschuk

Institution: Russian Academy of Sciences

Language: English

Topic: Climate change and permafrost

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: diamond

OpenAlex ID: W4405978541

FWCI:

Cites: 23

Cited by:

Related to: 10