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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection
Article describes how the low-cost sensor has changed the air quality monitoring paradigm with the capacity for efficient network expansion and community engagement. This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine.
Date:
June 27, 2022
Creator:
Liang, Lu & Daniels, Jacob
System:
The UNT Digital Library