title: Pollutant intrusion modeling in water distribution networks using artificial neural networks.
abstract: The development and implementation of water quality models for water distribution systems have been growing interest for both environment and hydraulic researchers. It is imperative that the system is able to distribute disinfectants and/or chemicals efficiently for specified quality standards and recover the actual quality of water in case of intrusion of a pollutant into the distribution network. The present work presents hydraulic and quality analysis in a typical water distribution system to obtain the concentration at the sources (pumping station or tanks) affected by typical pollutants utilizing water quality at monitoring points as inputs to artificial neural network (ANN) model. The universal function approximation property of the ANN architecture is being employed for inverse mapping to predict the water quality at the source using the water quality at arbitrary monitoring locations in the distribution system. The optimal monitoring points are identified by water age analysis. The performance evaluation results are encouraging and demonstrate the potential applicability of the methodology.
authors.affiliation: Department of Civil Engineering, MNNIT Allahabad, Allahabad-211 004, India. rajm@mnnit.ac.in
authors.forename: Raj Mohan
authors.initials: RM
authors.lastname: Singh
authors.forename: Akhouri Ishan
authors.initials: AI
authors.lastname: Rahul
mesh_terms: D000465:Algorithms; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000379:methods*; D007700:Kinetics; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D014867:Water / Q000737:chemistry*; D014873:Water Pollutants / Q000032:analysis*; D018508:Water Purification / Q000379:methods; D060753:Water Quality; D014881:Water Supply / Q000032:analysis / Q000592:standards
chemical_list: D014873:Water Pollutants; D014867:Water