首页|Southern Nevada Water Authority Reports Findings in Cyanobacteria (Classificatio n machine learning to detect de facto reuse and cyanobacteria at a drinking wate r intake)
Southern Nevada Water Authority Reports Findings in Cyanobacteria (Classificatio n machine learning to detect de facto reuse and cyanobacteria at a drinking wate r intake)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Gram-Negative Bacteria - Cyanobacteria is the subject of a report.According to news reporting origina ting from Henderson, Nevada, by NewsRx correspondents, researchstated, “Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levelsof certain contaminants at drinking water intakes. Therefore, the go al of this study was to use multiclasssupervised machine learning (SML) classi fication with data collected from six online instrumentsmeasuring fourteen tota l water quality parameters to detect cyanobacteria (corresponding to approximately 950 cells/mL, 2900 cells/mL, and 8600 cells/mL) or DFR (0.5, 1 and 2 % for wastewater effluent) eventsin the raw water entering an intake.”
HendersonNevadaUnited StatesNorth and Central AmericaBiological FactorsBiological PigmentsCyanobacteriaCyb orgsEmerging TechnologiesGram-Negative BacteriaGram-Negative Oxygenic Phot osynthetic BacteriaMachine LearningPhycocyanin
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