ISBN-13: 9783844329957 / Angielski / Miękka / 2012 / 404 str.
The difficulty in knowledge representation of a Water Distribution Network (WDN) problem has contributed to the limited use of Artificial Intelligence (AI) based expert systems (ES) in the management of these networks. This book presents an attempt to develop an Expert System that incorporates a dynamic knowledge acquisition system driven by simulated runs of a hydraulic model, suitably calibrated and validated for the given water utility. The Expert System integrates computational platforms such as MATLAB, open source GIS, CLIPS (an expert system tool developed by NASA's Software Technology Branch), and a Relational Database Management System (RDBMS) working under the umbrella of a common User Interface. The User Interface has been designed as a PC based application using Visual Studio .Net programming language. The proposed ES has an inbuilt Calibration module that enables calibration of an existing (aged) WDN for the unknown, and unobservable, Hazen-Williams C-values. In addition, the Daily Run and Simulation modules of the proposed ES further enable the CLIPS inference engine to evaluate the network performance for any emergent or suggested test scenarios.
The difficulty in knowledge representation of a Water Distribution Network (WDN) problem has contributed to the limited use of Artificial Intelligence (AI) based expert systems (ES) in the management of these networks. This book presents an attempt to develop an Expert System that incorporates a dynamic knowledge acquisition system driven by simulated runs of a hydraulic model, suitably calibrated and validated for the given water utility. The Expert System integrates computational platforms such as MATLAB, open source GIS, CLIPS (an expert system tool developed by NASAs Software Technology Branch), and a Relational Database Management System (RDBMS) working under the umbrella of a common User Interface. The User Interface has been designed as a PC based application using Visual Studio .Net programming language. The proposed ES has an inbuilt Calibration module that enables calibration of an existing (aged) WDN for the unknown, and unobservable, Hazen-Williams C-values. In addition, the Daily Run and Simulation modules of the proposed ES further enable the CLIPS inference engine to evaluate the network performance for any emergent or suggested test scenarios.