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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
A Low-Cost, Open Source Monitoring System For Collecting High Temporal Resolution Water Use Data On Magnetically Driven Residential Water Meters, Camilo J. Bastidas Pacheco, Jeffery S. Horsburgh, Robb J. Tracy
A Low-Cost, Open Source Monitoring System For Collecting High Temporal Resolution Water Use Data On Magnetically Driven Residential Water Meters, Camilo J. Bastidas Pacheco, Jeffery S. Horsburgh, Robb J. Tracy
Publications
We present a low-cost (≈$150) monitoring system for collecting high temporal resolution residential water use data without disrupting the operation of commonly available water meters. This system was designed for installation on top of analog, magnetically driven, positive displacement, residential water meters and can collect data at a variable time resolution interval. The system couples an Arduino Pro microcontroller board, a datalogging shield customized for this specific application, and a magnetometer sensor. The system was developed and calibrated at the Utah Water Research Laboratory and was deployed for testing on five single family residences in Logan and Providence, Utah, for …
Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data In A Large River Basin, Hervé Guillon, Colin F. Byrne, Belize A. Lane, Samuel Sandoval Solis, Gregory B. Pasternack
Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data In A Large River Basin, Hervé Guillon, Colin F. Byrne, Belize A. Lane, Samuel Sandoval Solis, Gregory B. Pasternack
Publications
Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to …