IBM Research: Using analytics to get smart about water

Ernesto Arandia, a data scientist with the IBM Smarter Water team in Dublin, Ireland, grew up half a planet away, in land-locked Bolivia. It was this life high in the Andes, where water availability is an issue for all citizens that spurred his interest in water management, and the pursuit of a PhD in Hydraulic Science at the University of Cincinnati.

He parlayed his research and creation of tools to manage water usage via smart meters and demand forecasting into a job with IBM Research-Ireland in 2013. Today, he is working on two research applications, a Water Network Analytics Toolkit and iWIDGET. Both of which deal with water demand and management by reducing cost and waste.

EA: My interests are in modeling urban water and environmental systems, stochastic processes, and data mining, as well as the design and optimization of hydraulic and civil infrastructure. With my civil and environmental engineering background, the IBM lab in Dublin seemed the perfect place to pursue my research on sustainability projects in the area of water management.

EA: My research in Cincinnati involved experimental and computational research for accurate estimation of real-time water usage, measured by smart meters. It was part of an EPA-funded project for the development of a contaminant warning system after 9/11, which required modelling contaminant transport and water quality in water distribution systems. The main goal was to develop a fine resolution model capable of predicting how water is used in an urban water network across space and time.

The experimental part involved setting up fixed monitoring stations to collect smart meter data from thousands of utility customers. Cincinnati Water Works was one of the first utilities in the US to fully adopt smart meters in their metro area – which created a unique research opportunity for me.

My first challenge was to find areas where the stations could monitor the consumption of a sample of users as large and as diverse as possible. This required a Geographic Information Systems (GIS) analysis of topography, demographics and socio-economics. The data collection stage took about two years and provided a dataset rich enough to understand, characterize, and represent water usage.

One of the main outcomes of the project was a model for synthetic generation of demands that vary periodically and resemble real smart meter demands. Another outcome was a model to forecast water demand aggregated at useful scales, which can be used to estimate and forecast water consumption in an urban water network in real time.

Pictured Smarter Water Team L-R:
Joe Sawaya-Naoum, Ernesto Arandia,
Bradley Eck and Fabian Wirth

EA: Our Smarter Water team entered a challenge posed by the Water Distribution Systems Analysis Conference called the Battle of Background Leakage Assessment for Water Networks (BBLAWN). BBLAWN asked for a methodology for recommending changes to the design and operation of a water distribution system to minimize total expenditure, while meeting service requirements.

Our entry paper, called A Simulation-Optimization Approach for Reducing Background Leakage in Water Systems, detailed a hydraulic model that simulates background leakage, custom implementations of heuristic algorithms, and optimization solvers. The solution methodology we proposed decomposed the overall problem into smaller more tractable problems aimed at a single type of decision. We found that examining each problem individually had the advantage of simplifying implementation of software and interpretation of results, and allowing parts of the problem to be examined in parallel.

The annual expenditure of the simulated town (called “C-Town”) was €3.9 million, but our models were able to reduce this to €1.5 million. This took into account both capital expenditure (rehab and infrastructure such as replacing pumps, pipes, valves, tanks) and operational expenditure (electricity costs as well as environmental penalties for wastage). Our main recommendation to "C-Town" was to invest about €600,000 to correct background leakage issues. This greatly reduced the amount of water loss in the network, as well as costs such as environmental penalties. This solution illustrates what a city could achieve in terms of managing leakage and overall operations.

Our work paid off as we won the competition for having the best combination of solution methodology, and economy of proposed infrastructural and management changes.

What are you working on now?

EA: I am currently working on a Water Networks Analytics Toolkit. It provides decision support for a range of operational and planning problems on water networks and building blocks for new solutions. It's a web-based application that knows the optimal time to replace mechanical equipment such as pumps and meters; calculates water bills under various pricing schemes; and makes adaptive water pricing recommendations.

I am also involved with a project called iWIDGET, which uses analytics and smart meters to gather real-time data on water and related energy usage. The aim of the project is to improve the management of urban water demand by reducing waste, providing utilities with deeper insights into end-user demand, and ultimately reducing customer water and energy costs.

iWIDGET provides information to householders to help them reduce their water usage, and also provides utility companies better visibility of their customers’ usage. This improved visibility lets the utility perform more accurate demand forecasting, and alert customers of suspected leaks.

EA: Water is a constrained resource, so utilities have the dual task of reducing losses through leaks in the network as well as reducing overall consumption. The best way to tackle these issues is by integrating government regulation, customer behavior and the utility’s own approach to improving infrastructure and measurement of usage.

Water utilities can learn from the energy and utilities sector, as it has a more highly developed model for pricing and demand management. The energy sector uses, for instance, an effective pricing mechanism based on “time of use” which means consumers use less power at peak times to reduce the chance of an outage.

Energy suppliers and distributors are also more proactively optimising their networks, using demand forecasting to balance energy supply (including renewables) with consumer demand. The water utilities should adapt and add to these components from the energy model instead of trying to reinvent the wheel.

EA: I see great opportunities to do joint research with scientists working in the energy domain. I think this collaboration can lead to new and more effective methodologies for water and energy management. It can also yield new combined strategies to improve water and energy efficiency. The data to support such cross-discipline research is increasingly becoming available and it is well known that systems cannot be fully described in total isolation. For instance, planning the optimal rehabilitation of a water network cannot be approached effectively with disregard for optimal electricity consumption.

I feel privileged to work in an environment that offers an abundance of human and material resources of quality to address meaningful projects and to conduct exciting and innovative research. I think that it is important for young scientists to take the knowledge and solutions they developed and bring them back to their origins, in my case Latin America.

For instance, in Bolivia, we do not have a continuous supply of water, which impacts our poorer citizens the most. I think that the work I have done can help make a difference. Technology that has been developed in advanced economies can be brought to developing countries and make an impact on their economies and quality of life.