Artificial Intelligence models in solving Ill-posed Inverse problems of Remote Sensing GHG emission

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dc.contributor.authorSylantyev, Sergiy
dc.contributor.authorYailymova, Hanna
dc.contributor.authorShelestov, Andrii
dc.date.accessioned2022-11-07T11:47:32Z
dc.date.available2022-11-07T11:47:32Z
dc.date.issued2022
dc.description.abstractenEarth’s atmosphere and surface is undergoing rapid changes due to urbanization, industrialization and globalization. Environmental problems such as desertification, soil depletion, water shortages, greenhouse gas (GHG) emissions warming the atmosphere, are increasingly significant and troubling consequences of human activities. UNEP forecast that under current policy, GHG emission will reach 60 gigatons CO2 per year by 2030. On the COP26 António Guterres said, what “We must accelerate climate action to keep alive the goal of limiting global temperature rise to 1.5 degrees”, and it is time to go “into emergency mode”. Before today a total of 33 relevant satellite missions with spectrometers like SAM, SAGE, GRILL, ATMOS, HALOE, POAM, GOMOS, MAESTRO was used for GHG monitoring capabilities from space underpinning the dynamic analysis and forecasting by solving ill-posed inverse problems on the bases GHG atmospheric measurement. Most of the practice science problems of atmospheric measurement emission gases are formally reduced to the Fredholm integral equations of the first kind. When numerically solving the Fredholm integral equation of a first kind, with which ill-posed inverse problems are associated, most of the problems of forecasting the dynamics of greenhouse gas emission, and other problems of forecasting the dynamics of atmospheric gases are reduced to solving a system of algebraic equations. In most cases, direct calculation of the kernel function of the Fredholm integral equation is impossible due to the lack of information on the parameters of the interaction of the spectrometer with the atmospheric measurement environment. As a consequence, the algorithm for solving the inverse ill-posed problem associated with forecasting the dynamics of greenhouse gas emission can be based on the use of machine learning and artificial intelligence methods. Moreover, taking into account the stochastic nature of the behavior of both atmospheric parameters and the measurement errors of spectrometers in inverse ill-posed problems, it is necessary to search not a single solution, but the distribution of the probabilities of solutions.uk
dc.format.page1 sl.uk
dc.identifier.citationSylantyev, S. Artificial Intelligence models in solving Ill-posed Inverse problems of Remote Sensing GHG emission / S. Sylantyev, H. Yailymova, A. Shelestov // Living Planet Symposium, 23-27 May 2022, Bonn, Germany.uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/50884
dc.language.isoenuk
dc.publisherLeaving Planet Symposiumuk
dc.sourceLiving Planet Symposium, 23-27 May 2022, Bonn, Germanyuk
dc.titleArtificial Intelligence models in solving Ill-posed Inverse problems of Remote Sensing GHG emissionuk
dc.typeOtheruk

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