Spotlight | Not all AI has the same IQ
When it comes to streamflow forecasting, can AI models beat existing approaches? Marshall Moutenot of Upstream Tech discusses which problems AI is a good fit for, when it’s not a great choice, and how to apply it most effectively
To jump to the chase, the competition wrapped and the conclusion is: yes, AI is a powerful tool when it comes to forecasting the amount of water that will flow through a river or stream. HydroForecast won 23 of 25 categories across all of the forecasting regions, validating to have such a decisive result validated by RTI International. After some reflection on the competition, I realized that there is a more interesting takeaway: other participants also used machine learning and, if you’ll excuse the double-negative, they didn’t just not win but in most cases performed worse than the traditional approaches to forecasting streamflow. Woah! Let’s dig deeper into this competition and
forecasting streamflow more broadly to better understand which problems AI is a good fit for, when it’s not a great choice, and when it is how to apply it most effectively.
Above: Visiting the Big Thompson at Estes Park USGS gauge location, one of USBR’s locations in the competition
THE LAST DECADE HAS seen a meteoric rise in applications of artificial intelligence (AI), and more specifically machine learning (ML) in our day-to-day lives, from our phones’ speech-to-text to the software we use to manage critical infrastructure. This proliferation of ML is in part largely due to
organizations’ storage and curation of massive amounts of data, ranging from instrumentation of marketing campaigns to sensors on a generator measuring vibrations. These large datasets are the necessary precursors to the application of ML. My team at Upstream Tech relies heavily on ML in our work, particularly with HydroForecast. We’re able to do this because of the existence of aforementioned “large datasets.” In our case, these are archives of meteorological forecasts, satellite imagery of basins, and in situ government and customer gauge records. These data combine with hydrological theory and ML to result in the most accurate operational forecasts of their kind. You must be wondering - “most accurate?” - a bold claim!
Recently, the HydroForecast team competed
in a year-long streamflow forecasting competition hosted by The Centre for Energy Advancement through Technological Innovation’s Hydropower Operations and Planning Interest Group. One goal of this competition was to determine whether AI models could beat existing approaches to forecasting streamflow.
AI or Nay-I Powerful, “new” technologies are sometimes billed as
silver bullets and panaceas; “they’ll solve your hardest problems and increase the efficiency of your strongest teams by 51%!”
When first assessing a problem, I adhere to an adage my engineering degree drilled into me: “keep it simple, stupid,” a design principle originally noted by the U.S. Navy in the 60s that, in my interpretation, asks “is there a creative, simple solution to this problem, even if it sacrifices some performance or accuracy?” In the case of “nascent” technologies like AI, there
is often a more discernible solution to a problem. Solutions devised from this viewpoint can often be more easily explained and maintained than their more complex counterparts. In the case of streamflow forecasting, organizations can and have gotten mileage out of simple regression or long-term averages. Depending on the problem being solved with the forecasts, if the tolerance for error is high enough, these simpler approaches are easy to interpret and maintain. However, if reducing error means safer, more efficient operations and more prescient planning, as it often does in the case of streamflow forecasting, sometimes simple solutions aren’t enough. It’s only in this case, once we’ve exhausted the candidacy of simple, creative solutions, that we’ll reach into our toolbox for machine learning.
Terminology AI is the broad class of programs and models that mimic cognitive functions that we associate with human minds, such as learning, adapting and reasoning. ML is a subset of AI, and describes a class of algorithms that are trained on data to produce answers.
8 | June 2022 |
www.waterpowermagazine.com
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