In practice, environmental analytics involves an integration of science, methods, and techniques involving a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to address “real-world” environmental and sustainability problems. Effective environmental decision-making is often challenging and complex where final results often involve inherently subjective political and socio-economic facets. Furthermore, while certain environmental and sustainability decision-making specifications may be self-evident (post hoc analysis always tends to be incredibly accurate), more typical problems possess components that cannot be directly included in the underlying decision process without additional manipulation. Such decision-making is frequently further compounded by additional stochastic uncertainties. Consequently, complex “real world” sustainability problems frequently employ computational decision-making approaches to construct solutions to applications containing numerous quantitative dimensions and considerable sources of uncertainty.