首页|Data on Machine Learning Reported by Adriel Bilharva da Silva and Colleagues (Pa rticulate matter forecast and prediction in Curitiba using machine learning)
Data on Machine Learning Reported by Adriel Bilharva da Silva and Colleagues (Pa rticulate matter forecast and prediction in Curitiba using machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Curitiba, Braz il, by NewsRx journalists, research stated, “Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan a reas or when there is no compliance check for vehicle emission standards. Partic ulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population.” The news reporters obtained a quote from the research, “In this study, we analyz ed the interaction between vehicular emissions, meteorological variables, and pa rticulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5. Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly an d daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR ) model, and for forecast, we used the naive estimation as baseline. RF showed t hat on hourly and daily prediction scales, the planetary boundary layer height w as the most important variable, followed by wind gust and wind velocity in hourl y or daily cases, respectively. The highest PM prediction accuracy (99.37% ) was found using the RF model on a daily scale. For forecasting, the highest ac curacy was 99.71% using the LSTM model for 1-h forecast horizon wi th 5 h of previous data used as input variables. The RF and LSTM models were abl e to improve prediction and forecasting compared with MLR and Naive, respectivel y. The LSTM was trained with data corresponding to the period of the COVID-19 pa ndemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 20 22, in which the data show that there was greater circulation of vehicles and hi gher peaks in the concentration of PM2.5. Our results can help the physical unde rstanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment.”
CuritibaBrazilSouth AmericaCOVID-1 9 ModelCyborgsDisease ModelEmerging TechnologiesEpidemiologyMachine Le arningRisk and Prevention
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