ABSTRACT
This chapter discusses scientific uncertainty, fuzziness, and application of Stochastic-Fuzzy models in Urban Transit, Water Resources, Energy Planning, and in Education. It outlines scientific uncertainty in relation to optimal Decision Support Systems (DSS) management for development. Fuzzy Logic is a scientific methodology for handling uncertainty and imprecision. Fuzzy randomness simultaneously describes objective and subjective information as a fuzzy set of possible probabilistic models over some range of imprecision. The quantification procedure is a combination of established methods from mathematical statistics for specifying the random part and of fuzzification methods for describing the fuzzy part of the uncertainty. A relevant work on DSS under uncertainty is the investigation and characterization of combinations of effects of fuzzy and stochastic forms of uncertainty in urban transit time-scheduling. Expert and scientific DSS which enhance correct evaluation, analysis, and synthesis of uncertainty inherent in data management should be utilized at each level of developmental planning and execution.
