A case study of knowledge modelling in an air pollution control decision support system

Issue title: Binding Environmental Sciences and AI

Article type: Research Article

Authors: Oprea, Mihaela

Affiliations: Department of Informatics, Petroleum-Gas University of Ploiesti, Bd. Bucuresti Nr. 39, Ploiesti, Romania E-mail: mihaela@upg-ploiesti.ro

Abstract: Air pollution control in urban regions is one of the main directions of research in the environmental sciences. For each region the pollution causes and pollution dispersion are different, depending on the industrial activity, on vehicles traffic, on domestic sources and so on, as well as on the geographical location, temperature of the air, speed and direction of the wind, and other weather factors. Several mathematical models are used for the description of the relationships between environmental protection and meteorological factors. An alternative approach to the mathematical models is a knowledge-based approach, that integrate multiple sources of knowledge in a knowledge base. The paper describes a case study of knowledge modelling in an air pollution control decision support system that uses DIAGNOZA_MEDIU, a prototype expert system dedicated to air pollution analysis and control in urban regions. We have developed an ontology, AIR_POLLUTION, for the application domain. Several AI techniques were used in the knowledge modelling process. An artificial neural network provides predictive knowledge to the facts base, and a part of the rules from the rule base are extracted by using inductive learning.

Keywords: Air pollution control, knowledge modelling, ontology, inductive learning, expert system

Journal: AI Communications, vol. 18, no. 4, pp. 293-303, 2005