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Decision Making Under Deep Uncertainty With Fuzzy Algorithm in Framework of Multi-Model Approach: Water Pollution Risk Assessment Using Satellite Data

Decision Making Under Deep Uncertainty With Fuzzy Algorithm in Framework of Multi-Model Approach: Water Pollution Risk Assessment Using Satellite Data
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Author(s): Yuriy V. Kostyuchenko (Scientific Centre for Aerospace Research of the Earth, National Academy of Sciences of Ukraine, Ukraine), Yulia Stoyka (Institute of Hydrobiology, National Academy of Sciences of Ukraine, Ukraine), Iurii Negoda (Institute of Geological Sciences, National Academy of Science of Ukraine, Ukraine) and Ivan Kopachevsky (Scientific Centre for Aerospace Research of the Earth, National Academy of Sciences of Ukraine, Ukraine)
Copyright: 2019
Pages: 34
Source title: Environmental Information Systems: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-7033-2.ch020

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Abstract

Task of soft computing for decision support in field of risk management is analyzed in this chapter. Multi-model approach is described. Interrelations between models, remote sensing data and forecasting are described. Method of water quality assessment using satellite observation is described. Method is based on analysis of spectral reflectance of aquifers. Correlations between reflectance and pollutions are quantified. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality is making based on fuzzy algorithm using limited set of uncertain parameters. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated. Conclusions concerned soft computing in risk management are proposed and discussed. It was demonstrated, that basing on spatially distributed measurement data, proposed approach allows to calculate risk parameters with resolution close to observations.

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