Identification And Prediction Of Pipe Line Hazards In Urban Water Distribution System By GIS And Neural Network Model

Various physical, chemical and biological hazards associated with water distribution networks is a constant concern of the engineers and managers working in the water treatment and distribution sector. The uncertainties in the water distribution networks involve pressure drops, contaminations by harmful materials, leakages, corrosion etc. As the time and location of the hazard is hard to predict the spread of uncertainty remains undetected when and where it happens. To prevent such types of atrocities, experts are recommending automatic monitoring systems for water distribution network hazards. The present study is proposing a cognitive Multi Criteria Decision Making model (MCDM) to predict the related water parameters in real time and taking logical and scientific decisions about the intensity of the hazards and level of actions required to be undertaken. GIS framework is utilized to digitize the distribution network and to provide a spatial interactive display about the condition of the distribution network. The system utilizes the technical advancement of neural networks, mathematics of signal flow graph theory and decision making ability of Analytic Hierarchy Process to identify, analyse and predict the physical, chemical and biological hazards.