Expert System for Management of Water Distribution Network (WDN)
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Sandeep Kulshrestha et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7401-7412 Expert System for Management of Water Distribution Network (WDN) SANDEEP KULSHRESTHA* Research Scholar, IIT Delhi, India [email protected] RAKESH KHOSA Civil Engineering Department,IIT Delhi,Hauz Khaz, New [email protected] Abstract This paper describes a component of an ongoing research initiative to investigate the potential application of artificial intelligence in the development of an ‘expert’ decision support system for management of a water distribution network, WAMAN (WAter MANagement). The system aims to meet several concerns of modern water utility managers as it attempts to formalize operational and management experiences, and provides a frame work for assisting water utility managers even in the absence of expert personnel. Expert System 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 uses CLIPS ( Artificial Intelligence (AI) tool developed by NASA's Software Technology Branch ) and integrates computational platforms such as MATLAB, open source GIS, and a Relational Database Management System (RDBMS) working under the umbrella of a common User Interface. Key Words: CLIPS, EXPERT SYSTEM, MATLAB, GIS, RDBMS 1. Introduction Difficulties in managing a typical water supply system arise on account of (i) absence of a well defined policy framework, (ii) difficulty in gathering information that is coherent and objective because, as is often the case, information is based on individual perception and experience, (iii) complexity of a typical water supply system on account of a variety of control mechanisms, and (iv) frequent changes in the network topology [Leon et al.(2000)]. These reasons pose great difficulties in efforts to develop mathematical models of such inherently complex systems [Walski(1993)]. Further, municipal water distribution networks (WDN) are seldom new. As a result of the natural process of ageing, and with frequent interventions necessitated by our perceived need to meet various performance goals, water supply networks undergo physical changes that significantly impinge on their hydraulic responses. Often, these impacts result in performance levels that are well below expectations. Additionally, WDN are managed by experts, who, over the years of their association and responsibility, acquire an empirical knowledge of the system and, characteristically, this knowledge remains largely confined to their respective personal domains. In the event of any new information and/or emergence of a new problem, these experts apply simple heuristics to design corrective measures and cognitively seek to predict network performance. Understandably, therefore, the assurance of a satisfactory response of the study network to suggested interventions is often based more on hope rather than on a validated belief. In the present day scenario, managers of water utilities face numerous difficulties on account various reasons such as i) Expert scientific knowledge is not readily accessible; ii) Expert scientific knowledge is not available in user friendly manner; and iii) The tacit, undocumented expertise required for operation of a water supply system may be lost when experienced personnel leave due to retirement or transfer. These reasons pose great difficulties in efforts to develop mathematical models of such inherently complex systems [Walski (1993)]. Recent developments in artificial-intelligence technology make it possible, by encoding knowledge and reasoning, for a computer program to simulate human expertise in a narrowly
Transcript of Expert System for Management of Water Distribution Network (WDN)
Sandeep Kulshrestha et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7401-7412
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ISSN: 0975-5462 7412
