Hydromorphological Classification Using Synchronous Pressure and Inertial Sensing

Classification of river morphology is often based on hydromorphological units (HMUs) identified from field measurements. Established survey methods rely on expert judgment or collection of field point measurements. When used for HMU classification, these methods can suffer from high errors due to the variations in the sampling environment, causing low repeatability. In order to expedite field data collection and increase HMU classification accuracy, we propose a multisensory device, the hydromast. Each hydromast provides a new source of data to classify HMUs. The modules are inexpensive and highly portable, consisting of a synchronous array of commodity pressure and inertial sensors. Rapid, local changes in the flow field are recorded with absolute and differential pressure sensors. At the same time, slower depth-integrated flow signals are obtained from a small damped cylindrical mast, driven by vortex-induced vibrations. In contrast to existing passive flow measurement technologies, the hydromast uses fluid-body interactions to provide flow measurements. This allows for minimal signal processing and simple feature extraction. An array of three hydromasts was used to collect ten samples in three river HMUs with shallow depths and highly turbulent flows with smooth and rough beds. We investigated classification accuracy using single, dual, and triple hydromast arrays with pressure, inertial, and combined features using linear regression, a genetic algorithm, and a neural network. Although limited in scope, the set of spot measurements covering three HMUs showed that a single multimodal sensor could deliver an overall classification accuracy of 89% of the HMUs, and an increase of up to 99% was achieved using a multimodal triple hydromast array. These preliminary results show promise in using hydromasts for rapid and robust HMU classification, providing a new way to collect and assess river survey data.