Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone

Abstract

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.

Keywords

• CO₂
• CH₄
• hydrocarbon emission prediction
• multimodal sensor
• machine learning
• XGBoost