Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems. (June 2021)

• Record Type: Journal Article
• Authors: Pascual-Pañach, Josep; Cugueró-Escofet, Miquel Àngel; Sànchez-Marrè, Miquel
• Is Part Of: Environmental modelling & software. Volume 140(2021)
• Publication Date: 2021-06
• Subjects: Case-based reasoning -- Rule-based reasoning -- Intelligent environmental decision support system -- Intelligent process control -- Data mining -- Wastewater treatment plant; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105021
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique —Case Based Reasoning (CBR)— and model-driven technique —Rule Based Reasoning (RBR)— for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations of environmental systems. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDS methodology. Furthermore, the IDS methodology is flexible and dynamic enough to be able to cope with the dynamic evolution of environmental systems, learning from its relevant experienced situations. The approach presented has been implemented in a real facility.