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Intelligent Urban Watersheds

Posted by haley.hughes | March 20, 2018

City buildingsOver the last few decades, hydraulic and hydrologic modelers have dramatically increased our understanding of urban watersheds; namely the built wastewater and stormwater infrastructure within their respective urban environments. These models have been manually tuned and calibrated using data from flow meters and other sensors, and adjusting available software knobs and levers to improve model accuracy, leading to better capital infrastructure planning. Additional, substantive improvements to the various modeling software platforms in recent years have yielded improved results and a more compelling user experience.

Recently, however, giant leaps forward in computing power, combined with advances and cost reductions in sensor and telemetry technologies, have made it possible to go far beyond the status quo and break into a new echelon of opportunities. We can now run high resolution models in real time, with real world precipitation data, while correcting critical downstream model nodes with observed sensor data. The outcome is perpetually calibrated digital copies of the urban watershed for far more effective real time operational decision making and control.

The Ideal

What if we had a hyper-accurate model of the urban watershed, combined with high quality real-time and forecast data on a continuous basis? What if this model/data platform was self-learning with powerful memory, whose watershed understanding improved with each wet weather event impacting the city and automatically recalibrated with changing conditions and new infrastructure?  What if the same platform was bi-directionally connected to all critical assets throughout the urban watershed (gates, valves, pumps, tanks, tunnels, plants, etc.) via powerful edge computing networks?  We could then fully understand the impact of what has happened in the recent past, what is happening now, and what is about to happen; all combined with the power and knowledge of what best to do about it.

Welcome to the Future

With the combined power of the Internet of Things, Big Data Analytics, machine learning and advanced control theory algorithms, the above vision is here today. Welcome to the Intelligent Urban Watershed. With it we can explore and stretch what is possible with immense understanding and optimal control of existing infrastructure.

Robust digital copies of the urban watershed, bombarded with real time sensor and forecast data, provide powerful continuous modeling to optimally manage critical assets. The same platform can simultaneously run multiple future exploratory models of infrastructure solution sets for planning considerations. This approach greatly improves daily operations of collection system infrastructure and provides an operational knowledge aggregator to absorb all institutional knowledge from operators, planning engineers, and utility leadership. Perhaps best of all, Intelligent Urban Watersheds can generate hundreds of millions, and sometimes billions of dollars in CIP savings by getting the highest performance, capacity utilization and resiliency from legacy infrastructure.

How is this possible?  Advances in artificial intelligence now allow modelers to supplement sub-catchments in the model with data trained nodes. Doing so increases the model efficacy for these sub-catchments to within +/- 5 percent accuracy relative to observed data, and can subsequently raise the overall model accuracy to +/- 10 percent, 80 percent or more of the time. We no longer need to build so much conservatism into future conditions models to account for uncertainty based on poorly calibrated areas where the urban hydrology is particularly complex. We can now select the level of safety buffer or slack for future variations in urban growth and shrinkage with improved confidence of an optimal outcome.

Continue reading in Meeting of the Minds

Originally written by Tim Braun

Photo Via Meeting of the Minds

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