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

Dedović, Maja Muftić; Avdaković, Samir; Turkovic, Irfan; Dautbašić, Nedis; Konjić, Tatjana

doi:10.1109/bihtel.2016.7775721

Cited by 27 publications

(12 citation statements)

References 20 publications

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“…The proposed model depended on an ANN model for training a dataset. In model evaluation, the proposed model output a forecasting result with 2 from 0.5 to 0.9 of total years [15]. Bing-Chun Liu et al, proposed an AQI forecast model to predict air pollution in three urban cities in China.…”

Section: Literature Reviewmentioning

confidence: 99%

One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level

Mahmood

Avşar

2021

MANAS Journal of Engineering

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With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentration for the next-day. The models make the prediction using concentrations of six atmospheric components (PM2.5, PM10, Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO)). The utilized machine learning methods are multilayer perception (MLP), Support Vector Regression (SVM), k-Nearest Neighbor (K-NN), Random Forests (RF), Gradient Boosting (GB), and Elastic Net (EN). After the predictions made by these models, the predicted values were further processed to be classified into one of the six air quality levels defined by United States Environmental Protection Agency. The prediction performances of the models as well as their corresponding classification results were analyzed. It was shown that MLP model gives the lowest RMSE of 2246 for prediction step while SVR achieved the highest accuracy score of 0.790.

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Section: Literature Reviewmentioning

confidence: 99%

One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level

Mahmood

Avşar

2021

MANAS Journal of Engineering

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“…Korištenjem neuronskih mreža razvijen je i testiran jedinstven model za predviđanje koncentracija N O 2 u vazduhu u Gradu Tuzla sa zadovoljavajućom regresijom dobivenih proračunatih podataka u odnosu na poznate podatke u posmatranom vremenskom periodu od 8 godina. Dobijeni rezultati, za posmatrani vremenski period, u poređenju sa rezultatima drugih autora su zadovoljavajući (Abd Rahman, Lee, Latif, & Suhartono 2013;Chaloulakou, Saisana, & Spyrellis 2003;Dedovic, Avdakovic, Turkovic, Dautbasic, & Konjic 2016;Lal Benjamin, Sharma, Pendharker, & Shrivastava 2014;Pandey, Srivastava, Sharma, & Srivastava 2014;Vlachogianni, Kassomenos, Karppinen, Karakitsios, & Kukkonen 2011). Na osnovu dobivenih grafičkih i analitičkih rezultata moguće je definisati mjere koje će dovesti do ispunjavanja zahtjeva o graničnim i tolerantnim vrijednostima čija bi impementacija imala veliki doprinos za poboljšanje kvaliteta vazduha i smanjenjen štetan utjecaj na okolinu i zdravlje ljudi u Gradu Tuzli.…”

Section: Zaključciunclassified

Neural network modeling methods for predicting the air parameters in the city of Tuzla

Agić¹,

Makić

Tadić

et al. 2020

J. eng. process. manag.

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According to the report of the World Health Organization, the city of Tuzla is the second in the world, and the first in Europe in terms of the number of diseases caused by air pollution. Tuzla Canton since 2003 has continuous air monitoring. Concentrations of individual pollutants exceed hourly, daily and annual limit values. In this paper, based on the existing results of air monitoring and meteorological data, using statistical methods and neural network modeling methods, unique and reliable models for predicting the concentration of NO2 in the air for the City of Tuzla have been developed. The results obtained using these models can be used in strategic decision-making processes and activities related to air quality control and management. This paper, on the example of the City of Tuzla, showed that using existing air monitoring data, concentrations of pollutants can be predicted for a longer period of time, using artificial intelligence methods. Reliable models with a high correlation coefficient can be obtained. In the case of a short or long interruption of the measurement of pollutant concentrations for the City of Tuzla with the help of models, which are the result of this work, it is possible to predict the concentrations of pollutants and plan to take measures based on them.

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“…One of the areas, in which these tools are more and more frequently used, is the problem of the air pollution, which is the topic of this work. In this case, they are are used in contamination forecasting …”

Section: State‐of‐the Art Studymentioning

confidence: 99%

New technologies for smart cities – high‐resolution air pollution maps based on intelligent sensors

Banach

Talaśka

Dalecki

et al. 2019

Concurrency and Computation

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Summary This paper presents a contribution in the development of a wireless sensor network, which can be used for building, in real time, dense air pollution maps, for compact urban areas. Such system may be useful for cyclists and pedestrians moving through the city. Based on such data, they can select the route in such a way, as to avoid the most polluted areas. An important step here may be development of miniaturized and cheap intelligent sensors, capable not only of data recording and transmitting, but also of some on‐site data processing and prediction. Such sensors require a development of small and power efficient circuit, including data processing unit integrated with an artificial neural network (ANN) in a single chip. We present a prototype chip that contains main components of such sensors, which include a programmable 10‐bit analog‐to‐digital converter, a programmable clock generator, and selected blocks of the ANN. The chip is a reconfigurable device, with many testing abilities. For this reason, one of the main challenges was a fast and efficient programming and testing tool. Such tool has been developed by us and is described in this work in detail along with selected measurement results. The presented work is an extended version of our conference paper (“Novel solutions for smart cities—creating air pollution maps based on intelligent sensors”).

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

Cited by 27 publications

References 20 publications

One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level

One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level

Neural network modeling methods for predicting the air parameters in the city of Tuzla

New technologies for smart cities – high‐resolution air pollution maps based on intelligent sensors

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