Abstract
The interdependencies among land use systems at national and global levels motivate the development of advanced systems analysis approaches for integration of land use models operating at different weights. The paper develops novel general approaches based on cross entropy principle for downscaling aggregate data and projections, which are robust with respect to feasible priors. Robust downscaling methods account for so-called non-Bayesian uncertainties, i.e., incomplete, unobservable, or erroneous information or data. In numerous case studies in China, African countries, Brazil, and Ukraine, the approaches allowed deriving local development projections of land use and land use change consistently with existing trends and expectations.
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*The studies are carried out within the framework of the EU-projects ECONADAPT (603906), TRANSMANGO (613532), AGRICISTRADE (612755) EU FP7, and the scientific project on the development of innovative methodologies and applications that investigate robust decisions for long-term coordinated planning of safe provisioning, energy and water supply, performed jointly by the International Institute for Applied Systems Analysis (Laksenburg, Austria) and National Academy of Sciences of Ukraine..
Translated from Kibernetika i Sistemnyi Analiz, No. 1, January–February, 2017, pp. 31–41.
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Ermoliev, Y.M., Ermolieva, T.Y., Havlík, P. et al. Robust Downscaling Approaches to Disaggregation of Data and Projections Under Uncertainties: Case of Land Cover and Land Use Change Systems* . Cybern Syst Anal 53, 26–33 (2017). https://doi.org/10.1007/s10559-017-9904-z
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DOI: https://doi.org/10.1007/s10559-017-9904-z