Computational Intelligence-based PM2.5 Air Pollution Forecasting

Oprea, Mihaela; Mihalache, Sanda Florentina; Popescu, Marian

doi:10.15837/ijccc.2017.3.2907

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Through analyzing and training a large number of historical data with machine learning technology, the machine learning prediction method obtains the prediction model that can track the changing trend of the PM2.5 concentration series. The typical representatives include shallow and deep artificial neural network model (ANN) [9][10][11][12], support vector machine model(SVM) [13] and least squares support vector machine model(LSSVM) [14]. Among various machine learning models, WNN has the advantages of multi-resolution of wavelet transform and the self-learning ability of the neural network.…”

Section: B Related Work About Prediction Modelsmentioning

confidence: 99%

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Wang

Chen

2022

IEEE Access

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Accurate and effective particulate matter 2.5(PM2.5) concentration prediction can provide early warning information for decision-making departments, so as to take governance and preventive measures. A combined model based on double-layer decomposition(DLD) and feedback of model learning effect for PM 2.5 concentration prediction is proposed in this paper. Firstly, ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) were used for double-level decomposition for the PM2.5 concentration series, to reduce the nonstationarity and nonlinearity of the concentration series and improve the predictability; Secondly, a Wavelet neural network (WNN) prediction model based on the feedback of model learning effect is established for the subsequence obtained by double-layer decomposition. Finally, the prediction results of each subsequence are superimposed to obtain the final prediction results. The case study shows that the prediction model proposed in this paper is scientific.

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Section: B Related Work About Prediction Modelsmentioning

confidence: 99%

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Wang

Chen

2022

IEEE Access

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“…The results of the mean absolute error of the ANFIS was less than 15% [9]. A similar approach used in [10], a study that was made to compare between ANNs and ANFIS to forecast PM2.5 for the next coming hours. The data collected from the Munich Station.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Air Pollution Forecasting Using Deep Learning

et al. 2021

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Nowadays, air pollution is getting an extreme problem that affects the whole environment. Due to the dangerous effects of air pollution on human’s health, this study proposes an air pollution prediction system. Because of the high dust pollution in Saudi Arabia, and the fact that there is no system for predicting the percentage of air pollution in it, this study applies an air pollution prediction system to the most affected area in Saudi Arabia. This paper aims to forecast the concentrations of PM10 particles due to their dangerous effects. This study aims to align with the Saudi vision 2030 by having an ideal environment and act in an efficient way in case of a warning situation. It applies a deep learning technique, which called Long Short-Term Memory (LSTM) to predict the air pollution in Saudi Arabia and achieved exceptional results due to the low error rates that have been obtained by this study. The error rate of Mean Absolute Error (MAE) is 0.98, for Root Mean Square Error (RMSE) is 8.68 and 0.999 for R-Squared.

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“…In literature, there are many theoretical studies and proposed solutions regarding air pollution monitoring [2], [3,4,7,9] . Many uses cases refer to small geographical areas where WIFI technology is also suitable.…”

Section: Introductionmentioning

confidence: 99%

Technical Solution for a Real-Time Air Quality Monitoring System

Simo

Dziţac

Frigura-Iliasa

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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This article will present a simple technical solution for a low-power and real-time air quality monitoring system. The whole package of software and hardware technical solutions applied for recording, transmitting and analyzing data is briefly described. This original monitoring system integrates a single chip microcon-troller, several dedicated air pollution surveillance sensors (for PM10, PM2.5, SO2, NO2, CO, O3, VOC, CO2), a LoRaWAN communication module and an online platform. This system was tested and applied under real field conditions. Depending on the measured values, it provides alerts, or, it can lead to the re-placement of specific components in the exhaust equipment. This article will pre-sent some experimental results, validated also by official measurements of government operated air quality stations.

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Computational Intelligence-based PM2.5 Air Pollution Forecasting

Cited by 13 publications

References 21 publications

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Air Pollution Forecasting Using Deep Learning

Technical Solution for a Real-Time Air Quality Monitoring System

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