Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

Brunelli, U.; Piazza, V.; Pignato, L.; Sorbello, Filippo; Vitabile, Salvatore

doi:10.1007/11731177_10

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…To partly address these concerns, artificial intelligence (AI) can offer an effective numerical approach to model complex and nonlinear relationships between a set of input data and targets, and it has been applied to many fields, from transport [27,28] to water resource engineering [29,30]. For air quality, artificial neural networks (ANN) can model nonlinear systems, and they have been successfully used to model sulfur dioxide concentrations in the industrial site of Priolo, Syracuse, Italy [31]. Comrie et al [32] compared multilayer perceptron (MLP) models with more traditional regression models for ozone forecasting.…”

Section: Introductionmentioning

confidence: 99%

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Phi

et al. 2019

Sensors

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Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.

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Section: Introductionmentioning

confidence: 99%

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Phi

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

Cited by 1 publication

References 6 publications

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

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