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The current state of landfills is an important issue for monitoring. There are a lot of municipal solid waste landfills in the world. But there are no standard solutions for monitoring the current state of these facilities both in Russia and abroad. It makes developing of technical solution, based on Autonomous sensors for measuring the concentrations of hazardous fumes, radiation background, geotechnical factors and other environmental variables, actual. Such system can be easily installed and can work in stand-alone mode for a long time. Decision-making technology in such system can be based on using of modern achievements of Machine Learning.

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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.