3 documents found, page 1 of 1
Air pollution forecasting using autoencoders: A classification-based prediction...
Rodríguez-García, María Inmaculada; Carrasco-García, María Gema; Cubillas Fernández, Paloma Rocío; Ribeiro, Conceição; Cardoso, PedroThis study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO2(t + 1), PM10(t + 1), and SO2(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected f...
Air pollution forecasting using autoencoders: a classification-based prediction...
Rodríguez-García, María Inmaculada; Carrasco-García, María Gema; Fernández, Paloma Rocío Cubillas; Ribeiro, Conceição; Cardoso, PedroThis study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO2(t + 1), PM10(t + 1), and SO2(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected f...
Air pollution PM10 forecasting maps in the maritime area of the Bay of Algecira...
Rodríguez-García, María Inmaculada; Carrasco-García, María Gema; Ribeiro, Conceição; González-Enrique, Javier; Ruiz-Aguilar, Juan JesúsPredicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, AN...