首页|Researchers at University of Florence Zero in on Machine Learning (Development a nd machine learning-based calibration of low-cost multiparametric stations for t he measurement of CO2 and CH4 in air)
Researchers at University of Florence Zero in on Machine Learning (Development a nd machine learning-based calibration of low-cost multiparametric stations for t he measurement of CO2 and CH4 in air)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from Firenze, Italy, by News Rx journalists, research stated, “The pressing issue of atmospheric pollution ha s prompted the exploration of affordable methods for measuring and monitoring ai r contaminants as complementary techniques to standard methods, able to produce high-density data in time and space.” Our news reporters obtained a quote from the research from University of Florenc e: “The main challenge of this low-cost approach regards the in-field accuracy a nd reliability of the sensors. This study presents the development of low-cost s tations for high-time resolution measurements of CO2 and CH4 concentrations cali brated via an in-field machine learning-based method. The calibration models wer e built based on measurements parallelly performed with the low-cost sensors and a CRDS analyzer for CO2 and CH4 as reference instrument, accounting for air tem perature and relative humidity as external variables. To ensure versatility acro ss locations, diversified datasets were collected, consisting of measurements pe rformed in various environments and seasons. The calibration models, trained wit h 70 % for modeling, 15 % for validation, and 15 % for testing, demonstrated robustness with CO2 and CH4 predictions achieving R2 v alues from 0.8781 to 0.9827 and 0.7312 to 0.9410, and mean absolute errors rangi ng from 3.76 to 1.95 ppm and 0.03 to 0.01 ppm, for CO2 and CH4, respectively.”
University of Florence, Firenze, Italy, Europe, Cyborgs, Emerging Technologies, Machine Learning
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