Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

2020 ◽  
Author(s):  
Mark B. Green ◽  
Linda H. Pardo ◽  
Scott W. Bailey ◽  
John L. Campbell ◽  
William H. McDowell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
High Frequency ◽  
Frequency Variation ◽  
Water Quality Sensors

SENSE IT - student created water quality sensors

2011 ◽  
Author(s):  
L. Hotaling ◽  
R. Stolkin ◽  
S. Lowes ◽  
P. Chen ◽  
J. Bonner ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Sensors

scholarly journals Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 86
Author(s):  
Angeliki Mentzafou ◽  
George Varlas ◽  
Anastasios Papadopoulos ◽  
Georgios Poulis ◽  
Elias Dimitriou
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Indicators ◽  
High Frequency ◽  
Automatic Monitoring ◽  
Water Levels ◽  
High Temporal Resolution ◽  
Water Quality Indicators ◽  
Pollution Levels ◽  
Riverine Ecosystems

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

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