Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.

Short-term prediction of NO_(2) and NO_(x) concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Introduction:Due to the health effects caused by airborne pollutants in urban areas,forecasting of air quality parameters is one of the most important topics of air quality research.During recent years,statistical models based on artificial neural networks(ANNs)have been increasingly applied and evaluated for forecasting of air quality.Methods:The development of ANN and multiple linear regressions(MLRs)has been applied to short-term prediction of the NO_(2) and NO_(x) concentrations as a function of meteorological conditions.The optimum structure of ANN was determined by a trial and error method.We used hourly NO_(x) and NO_(2) concentrations and metrological parameters,automatic monitoring network during October and November 2012 for two monitoring sites(Abrasan and Farmandari sites)in Tabriz,Iran.Results:Designing of the network architecture is based on the approximation theory of Kolmogorov,and the structure of ANN with 30 neurons had the best performance.ANN trained by scaled-conjugate-gradient(trainscg)training algorithm has implemented to model.It also demonstrates that MLP neural networks offer several advantages over linear MLR models.The results show that the correlation coefficient(R2)values are 0.92 and 0/94 for NO_(2) and NO_(x) concentrations,respectively.But in MLR model,R2 values were 0.41 and 0.44 for NO_(2) and NO_(x) concentrations,respectively.Conclusions:This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO_(2) and NO_(x) concentrations in an urban environment compared to linear models.