System learning approach to assess sustainability and forecast trends in regional dynamics: The San Luis Basin study, Colorado, U.S.A. (July 2016) - González-Mejía, Alejandra M.; Eason, Tarsha N.; Cabezas, Heriberto
Abstract: This paper presents a methodology that combines the power of an Artificial Neural Network and Information Theory to forecast variables describing the condition of a regional system. The novelty and strength of this approach is in the application of Fisher information, a key method in Information Theory, to preserve trends in the historical data and prevent over fitting projections. The methodology was applied to demographic, environmental, food and energy consumption, and agricultural production in the San Luis Basin regional system in Colorado, U.S.A. These variables are important for tracking conditions in human and natural systems. However, available data are often so far out of date that they limit the ability to manage these systems. Results indicate that the approaches developed provide viable tools for forecasting outcomes with the aim of assisting management toward sustainable trends. This methodology is also applicable for modeling different scenarios in other dynamic systems. Highlights: Novel methodology that combines principles of Artificial Neural Networks and Information Theory. A baseline scenario for the San Luis agricultural region was projected (1969–2025) with a sustainability constraint. Useful approach for sustainable management and decision making about consumption and production in complex human systems.