Operational analysis and medium-term forecasting of the greenhouse gas generation intensity in the cryolithozone
Timofeev A.V. Piirainen V.Y. Bazhin V.Y. Titov A.B.
November 2021MDPI
Atmosphere
2021#12Issue 11
We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO2, CH4) intensity in a certain area of the cryolithozone using data from a geographically distributed network of multimodal measuring stations. A network of measuring stations, capable of functioning autonomously for long periods of time, continuously generated a data flow of the CO2, CH4 concentration, soil moisture, and temperature, as well as a number of other parameters. These data, taking into account the type of soil, were used to build a spatially distributed dynamic model of greenhouse gas emission intensity of the permafrost area depending on the temperature and moisture of the soil. This article presented models for estimating and medium-term predicting ground greenhouse gases emission intensity, which are based on artificial intelligence methods. The results of the numerical simulations were also presented, which showed the adequacy of the proposed approach for predicting the intensity of greenhouse gas emissions.
CH4 , CO2 , Hydrocarbon emission prediction , Machine learning , Multimodal sensor , XGBoost
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Data Processing Laboratory, EqualiZoom LLC, Akzhol 2, Nur-Sultan, 010000, Kazakhstan
Department of Material Science and Technology of Art Products, Mechanical and Mechanical Engineering Faculty, Saint Petersburg Mining University, 2 21st Line, St. Petersburg, 199106, Russian Federation
Department of Automation of Technological Processes and Productions, Mineral Refining Faculty, Saint Petersburg Mining University, 2 21st Line, St. Petersburg, 199106, Russian Federation
Graduate School of Business Engineering, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University (SPbPU), St. Petersburg, 199034, Russian Federation
Data Processing Laboratory
Department of Material Science and Technology of Art Products
Department of Automation of Technological Processes and Productions
Graduate School of Business Engineering
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