Default reasoning and possibility theory
Artificial Intelligence
Non-monotonic set theoretic operations
Fuzzy Sets and Systems
Knowledge-Driven versus Data-Driven Logics
Journal of Logic, Language and Information
A framework for multi-source data fusion
Information Sciences: an International Journal - Special issue: Soft computing data mining
Uncertainty in the environmental modelling process - A framework and guidance
Environmental Modelling & Software
An introduction to bipolar representations of information and preference
International Journal of Intelligent Systems
Bipolar Queries Using Various Interpretations of Logical Connectives
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
International Journal of Geographical Information Science
Bipolar Fuzzy Mathematical Morphology for Spatial Reasoning
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
The canonical decomposition of a weighted belief
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Generic integration of environmental decision support systems - state-of-the-art
Environmental Modelling & Software
Handling bipolarity in elementary queries to possibilistic databases
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Bipolar representations in reasoning, knowledge extraction and decision processes
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Bridging uncertain and ambiguous knowledge with imprecise probabilities
Environmental Modelling & Software
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Editorial: A guide to expert opinion in environmental modelling and management
Environmental Modelling & Software
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Spatial assessment of environmental phenomena at regional/global scale involves the analysis and fusion of multiple, complex, multidisciplinary, and large-scale information. Since very often reliable models of such phenomena are lacking, the ''syndrome approach'' has been adapted to this purpose. In this context, there is a strong need for frameworks capable of handling data from heterogeneous sources in order to fuse them into synthetic indicators by modelling the uncertain and incomplete knowledge of the phenomenon. The approach here proposed models a syndrome by soft revision of bipolar information having heterogeneous role: a set of contextual conditions constraining the flourishing of the syndrome (negative information), identified on the basis of the expert's knowledge, and a typical pattern of notable symptoms (positive information) that are indeed proxies of observations of the syndrome occurrence. Specifically, three soft revision strategies are defined in the paper to combine negative and positive information whose overall aim is to define indicators of occurrence of an environmental syndrome with distinct objectives. The main concern of the first two revisions is to reduce the incoherence of bipolar information by taking into account the information reliability: the first strategy models a cautious decision attitude by eliminating incoherence and by modelling priorities of either negative or positive information and their partial trust, while the second strategy models a cautious and precautionary decision attitude giving credit to observations by taking into account False Negatives and False Positives. Finally, the third strategy models decision attitudes characterized by distinct level of risk. The proposed methodology is exemplified by a real case study whose objective is to define an indicator of the Rural Exodus syndrome for the Mediterranean region. In this case the method fuses biophysical related variables derived from Earth Observation (EO) data with geophysical and socioeconomic conditions based on the proposed strategies.