Forecasting PM10 concentrations using neural networks and system for improving air quality

2016 ◽  
Author(s):  
Maja Muftic Dedovic ◽  
Samir Avdakovic ◽  
Irfan Turkovic ◽  
Nedis Dautbasic ◽  
Tatjana Konjic
Keyword(s):  
Neural Networks ◽  
Air Quality

scholarly journals ARTIFICIAL NEURAL NETWORKS: AN APPLICATION IN THE STUDY OF AIR QUALITY

2014 ◽  
Vol 13 ◽  
Author(s):  
Amaury De Souza ◽  
Hamilton Germano Pavão ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Multilayer Perceptron ◽  
Artificial Neural

A estimativa da concentração do ozônio de superfície propicia a geração de dados para o planejamento de previsão da qualidade do ar, útil na gestão de saúde publica. O objetivo deste trabalho foi elaborar uma Rede Neural Artificial (RNAs) para estimar a concentração do ozônio de superfície em função de dados diários de clima. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência da concentração diária do ozônio medida. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. O desempenho da RNA desenvolvida foi muito bom, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da concentração do ozônio de superfície. O modelo proposto pode ser utilizado pelo governo público como ferramenta para ativar ações de ferramentas durante os períodos de estagnação atmosférica, quando os níveis de ozônio na atmosfera possam representar riscos à saúde publica.

Forecast of Air Quality Based on Ozone by Decision Trees and Neural Networks

2013 ◽  
pp. 97-106
Author(s):  
Nahun Loya ◽  
Iván Olmos Pineda ◽  
David Pinto ◽  
Helena Gómez-Adorno ◽  
Yuridiana Alemán
Keyword(s):  
Neural Networks ◽  
Air Quality ◽  
Decision Trees

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

scholarly journals Improving air quality management using gradient boosting based hierarchical temporal memory neural networks and fuzzy based classification based regression tree

2018 ◽  
Vol 7 (4) ◽  
pp. 12
Author(s):  
Sagayaraj S ◽  
Vetrivelan N
Keyword(s):  
Neural Networks ◽  
Air Quality ◽  
Meteorological Data ◽  
Regression Tree ◽  
Biological Molecules ◽  
Gradient Boosting ◽  
Temporal Memory ◽  
Hierarchical Temporal Memory ◽  
The Mean

In recent years, air pollution introduces different biological molecules, particulate and several harmful materials which affect the human health and activities. So, the quality of the air should be maintained for avoiding the above issues. To manage the air quality initially the meteorological data have been collected from Ariyalur that includes the condition of air, data collected date, high and low temperature, wind speed, wind direction and relative humidity. The collected data has to be preprocessed by applying the normalization and data mining techniques and those preprocessed data’s are used to predict the pollutants and the concentration level of the pollutants such as sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and nitric oxide (NO). Then the particulate matter level in the air has to be predicted by Gradient Boosting based Hierarchical Temporal Memory Neural Networks (BHTMNN). From the predicted value the strength of the pollutants is classified by using the Fuzzy based Classification based Regression Tree (FCART) which is used to recognize the disease arises in the human respiratory system. Then the performance of the proposed system is evaluated using the mean square error, classification accuracy, sensitivity and specificity.

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