Leveraging Big Data to develop an expert system for the optimal operation of smart water networks

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Aging drinking water systems (DWS) worldwide are under increasing pressure to reduce non-revenue water (NRW, estimated at over $14B USD/year), minimize energy costs, and cut greenhouse gas emissions (GHGs). However, the capital costs to replace this aging infrastructure, estimated by the American Water Works Association in 2019 to over $472B USD in the USA by 2039, is cost-prohibitive - triggering a need for innovative solutions to address these significant issues. Due to the latest developments in wireless sensors, many DWS are now collecting high-frequency streams of key variables (e.g., parcel level consumer water demand) at varying spatial and temporal scales, resulting in Big Data and prompting the conversion of traditional DWS to smart water networks (SWANs) that enable the mitigation of sub-optimal DWS operations (e.g., by minimizing energy usage). SWANs rely on expert systems based on data-driven models (e.g., machine learning) to identify optimal operational decisions that meet the goals of DWS managers, who struggle to sustainably operate DWS in the face of numerous uncertainties (e.g., changing water demand). However, SWANs are still in their infancy and there are no expert systems that can handle Big Data and account for uncertainty in DWS in a computationally efficient manner. My long-term vision is to optimize DWS through the use of SWANs. The next 5 years of my research program will focus on the most immediate needs to realize an expert system to address these important challenges. The expert system will be built sequentially through three short-term objectives: 1) novel state-of-the-art data-driven models will be explored for pre-processing and making accurate forecasts from Big Data associated with SWANs (e.g., parcel level consumer water demands); 2) these models will be incorporated in a novel stochastic framework to account for several important uncertainty sources (e.g., model structure) and temporal correlations to improve the reliability of the expert system; and 3) the previous two stages will be coupled with reinforcement learning for optimizing SWANs (with a focus on pump schedule optimization) according to key operational goals (minimizing NRW, energy costs, and/or GHGs). To demonstrate their superiority, the novel developments from each objective will be rigorously compared against current state-of-the-art methods and benchmark approaches adopted by water utilities. The proposed research will advance new knowledge on optimizing SWANs and provide a novel expert system for water utilities to address pressing challenges, with the potential to save utilities $4.6B USD/year in operation costs. Through my program, 8 student researchers will gain the skills necessary to make significant impacts at water utilities (e.g., City of Ottawa) and private firms invested in SWANs (e.g., Innovyze), contributing to the sustainable management of water resources and placing Canada at the forefront of research in SWANs.