Decades of underinvestment in critical urban infrastructure have forced U.S. cities into a tight corner. Bridges, roads, aqueducts, sewer pipes — the infrastructure that helped drive the last 30 years of growth is failing. To drive economic growth, cities need to manage complex, aging infrastructure efficiently and reinvest smarter.
Innovators in water infrastructure management are leading the way with investments that empower cities to use descriptive, predictive and prescriptive analytics to dynamically manage critical infrastructure.
Descriptive analytics is all about collecting and synthesizing data efficiently to create an accurate rendering of infrastructure assets. In water infrastructure management, developing up-to-date asset conditions is an arduous task requiring continuous inventories of miles of lakes and streams. To cut down on ineffective, paper-intensive inventories, managers are turning to GIS-integrated, tablet-based inspection tools.
The Santa Clara Valley Water District in Santa Clara County, Calif., manages a network of natural and man-made infrastructure that supplies 1.8 million residents with water. In an effort to go paperless, district field staff was armed with GIS tablets to survey waterway infrastructure, cataloging and assessing the condition of levees and other assets. These data are now fed back into the district’s asset management software, allowing the agency to not only see infrastructure conditions but to make smart decisions about future investments. According to Esri, more than 4,000 paperless inspections have been processed since 2012.
Predictive analytics allows managers to make smart decisions about the future by combining thousands of data points to create causal models of events. In Europe, four cities are experimenting with X-band radar’s capacity to predict flash floods by counting every raindrop as part of the RainGain project. This innovation isn’t just about weather reporting: Real-time quantified rainfall data has the potential to help cities dynamically predict floods and deploy infrastructure to curb damage.
Rotterdam, in the Netherlands, is mostly below sea level so the only way to remove the water is through a network of mobile water pumps and storage basins. Scientists there are running trials to link the real-time predictions from the X-band radar to the deployment of water pumps and management of storage basins.
Descriptive and predictive tools can be used to create a system that analyzes data, diagnoses problems and automatically makes prescriptions to optimize infrastructure and maximize efficiency.
After decades of costly inventories and generic maintenance schedules, Des Moines, Iowa, water treatment managers decided to embed meters, sensors and diagnostic equipment in the city’s treatment plant’s pumps and motors. Using automated logic controllers and an Infor EAM asset management system, these managers created a system that could collect data on motor performance, analyze it, diagnose inefficiencies and optimize the plant’s motors for maximum energy efficiency. The plant now uses 20 percent less energy than before, and generates smart capital improvement schedules that improve operations.
We can see in the most fascinating ways that the data infrastructure tools of today are helping governments at all levels extend the life and use of older, more traditional infrastructure.