Prediction of PM2.5 concentration based on recurrent fuzzy neural network

Zhou, Shanshan; Li, Wenjing; Qiao, Junfei

doi:10.23919/chicc.2017.8027970

Cited by 22 publications

(12 citation statements)

References 18 publications

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“…Ma et al [44] used neural network methods compared with traditional approaches to estimate PM 2.5 dispersion in broad geographical zones, reaching lower RMSE values than the traditional meteorological approaches. Furthermore, Tian-Cheng et al [45], Zhu et al [46], and Zhou et al [47] proposed ANN as an optimization method to predict PM2.5 concentration in outdoor environments. However, those works focus on different approaches and experimental tests significantly different from the ones reported in this study, making it challenging to compare the reported accuracy values.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions

2021

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Today, air quality is one of the global concerns that governments are facing. One of the main air pollutants is the particulate matter (PM) which affects human health. This article presents the modeling of a purification system by means of negative air ions (NAIs) for air pollutant removal, using computational intelligence methods. The system uses a high-voltage booster output to ionize air molecules from stainless steel electrodes; its particle-capturing efficiency reaches up to 97%. With two devices (5 cm × 2 cm × 2.5 cm), 2 trillion negative ions are produced per second, and the particulate matter (PM 2.5) can be reduced from 999 to 0 mg/m3 in a period of approximately 5 to 7 minutes (in a 40 cm × 40 cm × 40 cm acrylic chamber). This negative ion generator is a viable and sustainable alternative to reduce polluting emissions, with beneficial effects on human health.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions

2021

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“…Recently, researchers have adopted neural network models to approximate the nonlinearity of the concentrations. Zhou et al [12] utilized air pollutant CO, NO 2 , O 3 , SO 2 , PM2.5, and PM10 and meteorological data (temperature, relative humidity, wind speed, and wind direction). Because the concentration of PM2.5 exhibits complex nonlinear dynamics, the study proposed a recurrent fuzzy neural network (NN) for the prediction.…”

Section: Prediction With a Neural Network Modelmentioning

confidence: 99%

“…Table 2 shows the list of variables with annotations on their relevant types. Although most variables are frequently utilized features in studies [10,12,15,17,19,20], to our knowledge, there are no prior studies on developing a GPR model with topographic information, traffic information, ultraviolet information, and power plant operation information. Variables, such as wind direction and topographic categories, require further explanations because these two variables are converted into a set of dummy variables by the discretization.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model

Jang

Shin

Lee

et al. 2020

Sensors

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Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.

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“…At present, the major prediction method of PM2.5 concentration is to analyze historical data and to predict with the method of machine learning. So far, plenty of scholars have done a lot of work to predict PM2.5 concentration and attempted many modeling methods, such as Markov [1], SVM [2], Neural Network [3] [4], etc.. In recent years, with the surging of in-depth learning, deep learning models such as CNN [5], RNN [6], DBN [7] have been applied in prediction of PM2.5 concentration.…”

Section: Introductionmentioning

confidence: 99%

Interval-based Non-dimensionalization Method (IBNM) and Its Application in PM2.5 Grade Prediction

CHEN

Yan³

et al. 2022

Preprint

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Aiming at the disadvantages of rationality and adaptability of linear dimensionless method, as well as the complexity of constructing polyline and curve dimensionless method, this paper proposes an Interval-based Non-dimensionalization Method (IBNM). IBNM can be assembled in to polyline IBNM or curve IBNM based on the critical points formed by interval partition. Interval division can be classified based on the existing index data, which is scientific, reasonable, simple and practical. Taking example of the prediction of PM2.5 air quality grade in Guangzhou, this paper constructs four models to predict air quality grade, such as, Support Vector Regression (SVR), Random Forest, Extremely Random Tree, and Gradient Lifting Regression Tree model. Meanwhile, the data is processed with extremum method, polyline IBNM and curve IBNM. The results show that the accuracy of polyline IBNM and curve IBNM is better than extremum method.

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Prediction of PM2.5 concentration based on recurrent fuzzy neural network

Cited by 22 publications

References 18 publications

Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions

Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model

Interval-based Non-dimensionalization Method (IBNM) and Its Application in PM2.5 Grade Prediction

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