A Classification Approach for Monitoring and Locating Leakages in a Smart Water Distribution Framework

In a water distribution network, leakages have always remained a problem of significant importance as a large amount of water gets wasted since leakage is localized and repaired for its normal operation. Besides the traditional methods of identifying a leakage which takes a lot of efforts and incurs a huge cost with a low efficiency, the technological advancements has made it possible to develop a smart water distribution system that will capture the real time statistical values of the distribution network through integration of the information and communication technology (ICT) with the physical devices of the water pipeline structure. Further, machine learning techniques can be applied to these statistical parameters to develop a decision model to predict the future. This paper presents the statistical classification framework through support vector machine technique that extracts the pressure and flow values from different locations of the water pipeline network and classifies the features into the leakage or non-leakage condition. The mathematical simulation is done on the EPANET tool and the dataset is deployed on MATLAB for statistical classification.