Linking Climatic Variables with Colombian Development Indicators via Inductive Learning Tools
John Alexander Segura Sr., Hydrosciences Research Group, Bogotá, Colombia; and R. J. Brito, Y. R. Coronel, and N. Obregón
Within the Colombian Development Planning Exercise 2006-2019 national goals have been established pursuing, among others: (1) An economy that guarantees a major level of well-being; (2) a more egalitarian society; (3) a society with free and responsibles citizens; and (4) an efficient State oriented to citizen services. In this realm, a revision of strategies aimed at achieving these macro-objectives shows that both regional and global climatic effects are crucial for them. However, establishing such cause-effect relationships among these variables in not an easy endeavour due not only to high nonlinearities present in these processes, but also to complex behavior found in systems involved. Further, other difficulties arise due to huge amount of interrelated variables present in these big and pertinent databases. Therefore, this work is intended to mine such data sets in order to enhance knowledge discovery hidden in these indicators. Thus, climatic effects can be incorporated in proper Decision Support Systems (DSS) for natural resources management, present in any planning exercise. At the end, analysis of the discovered rules and knowledge by means of applied intelligent supervised algorithms, suggest that it is possible to improve efficacy and efficiency of strategies and programs during implementation phase of such planning.
Joint Session 3, Artificial Intelligence and Climate Applications (Joint between 5th Conference on Applications of Artificial Intelligence in the Environmental Sciences and 19th Conference on Climate Variability and Change)
Tuesday, 16 January 2007, 1:40 PM-5:00 PM, 210B
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