Monitoring Water Distribution Network using Machine Learning

Abstract: Water is an important natural resource. It is supplied to our home by water distribution network thatis owned and maintained by water utility companies. Around one third of water utilities across the globereport a loss of 40% of clean water due to leakage. The increase in pumping, treatment and operationalcosts are pushing water utilities to combat water loss by developing methods to detect, locate, and xleaks. However, traditional pipeline leakage detection methods require periodical inspection with humaninvolvement, which makes it slow and inecient for leakage detection in a timely manner. An alternativeis on-line, continuous, real-time monitoring of the network facilitating early detection and localization ofthese leakages. This thesis aims to nd such an alternative using various Machine Learning techniques.For a water distribution network, a novel algorithm is proposed based on the concept of dominantnodes from graph theory. The algorithm nds the number of sensors needed and their correspondinglocations in the network. The network is then sub-divided into several leakage zones, which serves as abasis for leak localization in the network. Thereafter, leakages are simulated in the network virtually,using hydraulic simulation software. The obtained time series pressure data from the sensor nodes ispre-processed using one-dimensional wavelet series decomposition by using daubechies wavelet to extractfeatures from the data. It is proposed to use this feature extraction procedure at every sensor nodelocally, which reduces the transmitted data to the central hub over the cloud thereby reducing the energyconsumption for the IoT sensor in real world.For water leakage detection and localization, a procedure for obtaining training data is proposed,which serves as a basis for recognition of patterns and regularities in the data using supervised Machinelearning techniques such as Logistic Regression, Support Vector Machine, and Articial Neural Network.Furthermore, ensemble of these trained model is used to build a better model for leakage detection andits localization. In addition, Random Forest algorithm is trained and its performance is compared tothe obtained ensemble of earlier models. Also, leak size estimation is performed using Support VectorRegression algorithm.It is observed that the sensor node placement using proposed algorithm provides a better leakage localizationresolution than random deployment of sensor. Furthermore, it is found that leak size estimationusing Support Vector Regression algorithm provides a reasonable accuracy. Also, it is noticed that RandomForest algorithm performs better than the ensemble model except for the low leakage scenario. Thus,it is concluded to estimate the leak size rst, based on this estimation for small leakage case ensemblemodels can be applied while for large leakage case only Random Forest can be used.