Deep Learning of Complex Pipe Leakages Events in Drinking Water Distribution Networks for Effective Spatiotemporal Pre-Detections and Isolations of Leak Conditions [report]

Cheng Ann Tan, Veradej Phipatanasuphorn, Chun Hin Adrian Lai
2020 Zenodo  
To detect pipes leakages over space and time of the water distribution in L-TOWN, this research study develops an alternative engineering tool, by combining the numerical capabilities of genetic algorithm and deep learning, which can pre-detect near and/or exact locations of pipe leakages within the water distribution network in L-TOWN over time. The genetic algorithm is programmed using an open-source Water Network Tool for Resilience (WNTR) in Python package. WNTR is an EPANET compatible
more » ... NET compatible Python version and is designed to simulate and analyze resilience of water distribution networks. For the deep learning component, a personalized feed-forward deep neural network (DNN) is built on Tensorflow platform to develop a trained predictive model using volumes of calibrated simulation data derived from WNTR based on the physical characteristics of the water distribution network in L-TOWN. The trained DNN model is then leveraged to predict the near and/or exact locations of pipe leakages in L-TOWN using the real-world measured data from the reported years of 2018 and 2019.
doi:10.5281/zenodo.3902945 fatcat:lvahfqn3wrdjvm6ku5bhjiqgju