Estimation of Prediction Error in Regression Air Quality Models

Hoffman, Szymon

doi:10.3390/en14217387

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…The RMSE statistic allows for a term-by-term comparison of the actual difference between the estimated and measured values, providing information on a model's short-term performance. Performance of the model improves as the value decreases (Hoffman 2021). The verification of experimental findings is frequently done in forecasting, climatology, and regression analysis.…”

Section: Idw Cross-validation Results For Pm25 Concentration Measured...mentioning

confidence: 98%

Spatial Distribution of Particulate Matter (PM_2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Ismain,

Salleh,

Mohammad Sham

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Particulate matter is one of the life threathening pollutants that are harmful to human health. The aim of this study is to assess PM2.5 distribution using spatial interpolation techniques of inverse distance weighted (IDW) by predicting their concentrations at distinct unmonitored locations. The IDW interpolation was developed using Arcmap for Klang Valley area covering six districts in central Peninsular Malaysia. The Department of Environment (DOE) continous air quality monitoring stations (CAQMS) were installed at 6 districts in Klang Valley throughout period of 9 months between January and September 2022 for data collection. The results from IDW showed that PM2.5 concentrations were highest in February and lowest in March. The IDW spatial mapping demonstrated that PM2.5 distributed higher in Seremban and Petaling districts throughout 9 months while Kuala Lumpur and Putrajaya demonstrated contrary lower. The IDW cross-validation results showed an acceptable predictive accuracy with low RMSE values ranging from 1.790 to 5.073 and high R squared value with range from 0.0267 to 0.5081. The results showed a very good fit of the IDW model to the observed points, confirming that the results of these analyses can monitor and predict PM2.5 concentrations with high accuracy. The interpolation maps that result can help identify key regions that require air quality management mitigation strategies.

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Section: Idw Cross-validation Results For Pm25 Concentration Measured...mentioning

confidence: 98%

Spatial Distribution of Particulate Matter (PM_2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Ismain,

Salleh,

Mohammad Sham

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Research can also be conducted on segment modeling. Since differences in modeling accuracy were found in different concentration subranges [66,67], the improvement of modeling quality was tested by replacing a single model with a group of models dedicated to specific subranges of pollutant concentrations [68]. Promising results were obtained for segmented modeling.…”

Section: Summary and Discussionmentioning

confidence: 99%

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Hoffman,

Jasiński,

Baran

2024

Energies

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Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station.

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“…The data also include two variables describing the time: day and hour. These two variables were converted to numeric form following the procedure described in [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

“…In regression modeling, it was found that the application of one neural network to the entire range of concentrations of the predicted pollutant resulted in different prediction accuracies in the concentration sub-ranges [ 38 , 39 ]. It was considered advisable to replace one neural network with several networks (sub-models), each of which would be adjusted to specific concentration sub-ranges [ 39 ]. The use of several sub-range models should improve the accuracy of the prediction.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Modeling with the Use of Regression Neural Networks

Hoffman

Filak

Jasiński

2022

IJERPH

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Air quality is assessed on the basis of air monitoring data. Monitoring data are often not complete enough to carry out an air quality assessment. To fill the measurement gaps, predictive models can be used, which enable the approximation of missing data. Prediction models use historical data and relationships between measured variables, including air pollutant concentrations and meteorological factors. The known predictive air quality models are not accurate, so it is important to look for models that give a lower approximation error. The use of artificial neural networks reduces the prediction error compared to classical regression methods. In previous studies, a single regression model over the entire concentration range was used to approximate the concentrations of a selected pollutant. In this study, it was assumed that not a single model, but a group of models, could be used for the prediction. In this approach, each model from the group was dedicated to a different sub-range of the concentration of the modeled pollutant. The aim of the analysis was to check whether this approach would improve the quality of modeling. A long-term data set recorded at two air monitoring stations in Poland was used in the examination. Hourly data of basic air pollutants and meteorological parameters were used to create predictive regression models. The prediction errors for the sub-range models were compared with the corresponding errors calculated for one full-range regression model. It was found that the application of sub-range models reduced the modeling error of basic air pollutants.

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Estimation of Prediction Error in Regression Air Quality Models

Cited by 4 publications

References 26 publications

Spatial Distribution of Particulate Matter (PM_2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Spatial Distribution of Particulate Matter (PM_2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Air Quality Modeling with the Use of Regression Neural Networks

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Estimation of Prediction Error in Regression Air Quality Models

Cited by 4 publications

References 26 publications

Spatial Distribution of Particulate Matter (PM2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Spatial Distribution of Particulate Matter (PM2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Air Quality Modeling with the Use of Regression Neural Networks

Contact Info

Product

Resources

About

Spatial Distribution of Particulate Matter (PM_2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Spatial Distribution of Particulate Matter (PM_2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model