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Articles 1 - 4 of 4
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 …
Simulation-Optimization For Conjunctive Water Resources Management And Optimal Crop Planning In Kushabhadra-Bhargavi River Delta Of Eastern India, Madan K. Jha, Richard C. Peralta, Sasmita Sahoo
Simulation-Optimization For Conjunctive Water Resources Management And Optimal Crop Planning In Kushabhadra-Bhargavi River Delta Of Eastern India, Madan K. Jha, Richard C. Peralta, Sasmita Sahoo
Publications
Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a …
Empirical Models For Predicting Water And Heat Flow Properties Of Permafrost Soils, Michael T. O'Connor, M. Bayani Cardenas, Stephen B. Ferencz, Yue Wu, Bethany T. Neilson, Jingyi Chen, George W. Kling
Empirical Models For Predicting Water And Heat Flow Properties Of Permafrost Soils, Michael T. O'Connor, M. Bayani Cardenas, Stephen B. Ferencz, Yue Wu, Bethany T. Neilson, Jingyi Chen, George W. Kling
Publications
Warming and thawing in the Arctic are promoting biogeochemical processing and hydrologic transport in carbon‐rich permafrost and soils that transfer carbon to surface waters or the atmosphere. Hydrologic and biogeochemical impacts of thawing are challenging to predict with sparse information on arctic soil hydraulic and thermal properties. We developed empirical and statistical models of soil properties for three main strata in the shallow, seasonally thawed soils above permafrost in a study area of ~7,500 km2 in Alaska. The models show that soil vertical stratification and hydraulic properties are predictable based on vegetation cover and slope. We also show that …
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 …