Machine learning non-contact radar changes hydrography
January 18, 2018
New technological advances are assisting hydrologists and hydrographers. A new line of non-contact radar sensors have the ability to accurately measure both velocity and level — and compute ﬂow — in real time. These capabilities are a game-changer for water authorities around the world.
Mounted safely high above the water, away from potentially damaging debris and fouling, the RQ-30 sensors are highly eﬀective for collecting data from sites previously thought to be too challenging or dangerous.
Data in real-time
The availability of real-time discharge data is critical to water authorities who devise optimal water management strategies and manage public safety.
The conventional method for developing a “stage-discharge rating” requires periodic site visits for recording discharge measurements using mechanical current meters or acoustic doppler current profilers (ADCP) for a range of stages. Complex flow conditions may negate stable stage-discharge ratings and make conventional methods impractical or impossible. Adverse conditions include flow reversals, backwater and hysteresis effects.
A smarter sensor
The RQ-30 incorporates both a water level sensor and a velocity Sensor in one compact enclosure. These two sensors simultaneously and continuously measure said parameters. Then, using known cross-section information stored in the sensor, discharge data can be computed in real time.
With the ability to provide discharge data from deployment, the RQ-30 begins to gather data at an unprecedented rate in addition to providing real-time, accurate water level, velocity and discharge data.
The radar sensor’s machine learning feature performs advanced pattern recognition, accelerating the building of a highly reliable internal rating curve. Rating curve development at a specific monitoring site can shave months of time off the conventional method.
Overcoming discharge measuring challenges
At low flow rates, wind can create surface waves by accelerating or decelerating the water surface. This effect can impact the accuracy of velocity and discharge measurements — especially in wide, deep, slow moving rivers. Deep channels with a calm, glassy surface tend to pick up wind effects easily. The innovation has the ability to remove wind noise from raw data.
To compensate for this and other environmental influences, the RQ-30 and its machine learning continuously build an internal relationship between water level and velocity. The more often a velocity or velocities are measured at a specific water level, the stronger the stage / discharge relationship for that specific level becomes.
The more often a relationship is “learned,” the stronger and more accurate the learning becomes. Thus, the RQ-30 can eliminate wind and other environmental effects in order to provide accurate flow data — even in challenging applications.
In addition to the radar sensor, Water Information System by KISTERS (WISKI) equips hydrologists with advanced discharge measurement and rating curve tools. This combination of sensor and software provides enhanced situational awareness for risk & emergency management.