Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2

Unnikrishnan, Reshma; Madhu, G.

doi:10.1007/s42452-019-1440-1

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…This research was used, as input, daily values of air temperature, humidity, www.nature.com/scientificreports/ The findings for each five air pollutants highlighted that neural network models to be significantly superior compared to MLR owing to their ability to effectively predict the complex and nonlinear issues such as air pollution 42 . Although implementation of regression methods is simple, however, the results of various studies also exhibit that regression methods may not offer precise predictions in some areas such as air pollution in comparison to ANNs models; and ANNs have generally superior performance 39,43,44 .The comprehensive data collection including green space and traffic data is the most advantages of our study. As a result, our ANN model is more accurate than other developed models in research.…”

Section: Discussionmentioning

confidence: 95%

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

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Section: Discussionmentioning

confidence: 95%

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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“…SO2, NOx, NH3, CO, particulate matter, T, RF (rainfall), RH, WD, WV (wind velocity) were used as inputs. Their model showed good quality evaluation characteristics with an MSE of 0.0115 and an R of 0.8979 [27].…”

Section: Resultsmentioning

confidence: 99%

Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

Kujawska

Kulisz

Aubakirova

2022

J. Phys.: Conf. Ser.

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Air quality control and its prediction are particularly important for human health and life. Sulfur dioxide constitutes one of the air pollutants that play an important role in air quality pollution. An artificial neural network model was employed to forecast the levels of sulfur dioxide in the air of Zamość (Poland). The measured data of the meteorological station of Zamość in 2017-2019 were used for the model. Temperature (T), relative humidity (RH), wind speed (WS), wind direction (WD), SO2, PM10, NO2, NOx, CO, O3, C6H6 were used as input parameters for building the neural network model. Regression value (R) and Mean Squared Error (MSE) were used to estimation the model. The results show that neural network is capable of predicting the sulfur dioxide levels in the air.

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“…Correlation analysis is used in [42] to select input variables for a CNN-LSTM model predicting the PM 2.5 concentrations. In [24], the authors examine both backward elimination and forward selection to choose the input variables, and the obtained results show that the models that utilize techniques to optimize the used input data perform better than those using all variables. Moreover, the backward elimination technique gives better results than the forward selection.…”

Section: Selection Of Input Variablesmentioning

confidence: 99%

“…These models are simpler, lighter, and easier to implement, and when enough historical data are provided, statistical models show better forecast results for specific locations. Popular statistical models for predicting the air quality include artificial neural networks [16][17][18][19][20][21][22][23][24], multiple linear regression [25], autoregressive integrated moving average (ARIMA) [26], support vector machine (SVM) [27], nonlinear regression [28] and random forest [29]. Neural networks (NN) are a popular choice for air pollution forecasting, since they do not require any prior assumptions about the data distribution, and are capable of modeling complex nonlinear processes.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction

2022

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Nowadays, air pollution is an important problem with negative impacts on human health and on the environment. The air pollution forecast can provide important information to all affected sides, and allows appropriate measures to be taken. In order to address the problems of filling in the missing values in the time series used for air pollution forecasts, the automation of the allocation of optimal subset of input variables, the dependency of the air quality at a particular location on the conditions of the surrounding environment, as well as automation of the model’s optimization, this paper proposes a deep spatiotemporal model based on a 2D convolutional neural network and a long short-term memory network for predicting air pollution. The model utilizes the automatic selection of input variables and the optimization of hyperparameters by a genetic algorithm. A hybrid strategy for missing value imputation is used based on a combination of linear interpolation and a strategy of using the average between the previous value and the average value for the same time in other years. In order to determine the best architecture of the spatiotemporal model, the architecture hyperparameters are optimized by a genetic algorithm with a modified crossover operator for solutions with variable lengths. Additionally, the trained models are included in various ensembles in order to further improve the prediction performance—these include ensembles of models with the same architecture comprising the best architecture obtained by the evolutionary optimization, and ensembles of diverse models comprising the k best models of the evolutionary optimization. The experimental results for the Beijing Multi-Site Air-Quality Data Set show that the proposed spatiotemporal model for air pollution forecasting provides good and consistent prediction results. The comparison of the suggested model with other deep NN models shows satisfactory results, with the best performance according to MAE, based on the experimental results for the station at Wanliu (16.753 ± 0.384). Most of the model architectures obtained by the optimization of the model hyperparameters using the genetic algorithm have one convolutional layer with a small number of kernels and a small kernel size; the convolutional layers are followed by a max-pooling layer, and one or two LSTM layers are utilized with dropout regularization applied to the LSTM layer using small values of p (0.1, 0.2 and 0.3). The utilization of ensembles from the k best trained models further improves the prediction results and surpasses other deep learning models, according to MAE and RMSE metrics. The used hybrid strategy for missing value imputation enhances the results, especially for data with clear seasonality, and produces better MAE compared to the strategy using average values for the same hour of the same day and month in other years. The experimental results also reveal that random searching is a simple and effective strategy for selecting the input variables. Furthermore, the inclusion of spatial information in the model’s input data, based on the local neighborhood data, significantly improves the predictive results obtained with the model. The results obtained demonstrate the benefits of including spatial information from as many surrounding stations as possible, as well as using as much historical information as possible.

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Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2

Cited by 10 publications

References 14 publications

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Application of artificial neural networks model to predict the levels of sulfur dioxides in the air of Zamość, Poland

A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction

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