scholarly journals Estimation of Prediction Error in Regression Air Quality Models

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7387
Author(s):  
Szymon Hoffman
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Multilayer Perceptrons ◽  
Prediction Errors ◽  
Approximation Accuracy ◽  
Entire Concentration Range ◽  
Modeling Tools ◽  
Single Measure ◽  
Monitoring Devices

Combustion of energy fuels or organic waste is associated with the emission of harmful gases and aerosols into the atmosphere, which strongly affects air quality. Air quality monitoring devices are unreliable and measurement gaps appear quite often. Missing data modeling techniques can be used to complete the monitoring data. Concentrations of monitored pollutants can be approximated with regression modeling tools, such as artificial neural networks. In this study, a long-term set of data from the air monitoring station in Zabrze (Silesia, South Poland) was analyzed. Concentration prediction was tested for the main air pollutants, i.e., O3, NO, NO2, SO2, PM10, CO. Multilayer perceptrons were used to model the concentrations. The predicted concentrations were compared to the observed ones to evaluate the approximation accuracy. Prediction errors were calculated separately for the whole concentration range as well as for the specified concentration subranges. Some different measures of error were estimated. It was stated that the use of a single measure of the approximation accuracy may lead to incorrect interpretation. The application of one neural network to the entire concentration range results in different prediction accuracy in various concentration subranges. Replacing one neural network with several networks adjusted to specific concentration subranges should improve the modeling accuracy.

scholarly journals Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Saini ◽  
Pratik Chaturvedi ◽  
Varun Dutt
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Economic Losses ◽  
Ensemble Model ◽  
Neural Network Models ◽  
Ensemble Techniques ◽  
Bidirectional Lstm

Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

Monitoring and Prediction of Air Quality using Neural Network

2018 ◽  
Vol 8 (6) ◽  
pp. 47
Author(s):  
Shwetal Raipure
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Pollution Index ◽  
Learning Technologies ◽  
Short Term ◽  
Initial Information ◽  
Term Forecast ◽  
The Neural Network

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are  several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network  can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is  a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the  neural network model has proposed.

Improved long-term temperature prediction by chaining of neural networks

2001 ◽  
Vol 11 (01) ◽  
pp. 1-10 ◽  
Author(s):  
M. DUHOUX ◽  
J. SUYKENS ◽  
B. DE MOOR ◽  
J. VANDEWALLE
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chain Structure ◽  
Multilayer Perceptrons ◽  
Temperature Prediction ◽  
Automatic Relevance Determination ◽  
One Step ◽  
Artificial Neural Network Ann

When an artificial neural network (ANN) is trained to predict signals p steps ahead, the quality of the prediction typically decreases for large values of p. In this paper, we compare two methods for prediction with ANNs: the classical recursion of one-step ahead predictors and a new kind of chain structure. When applying both techniques to the prediction of the temperature at the end of a blast furnace, we conclude that the chaining approach leads to an improved prediction of the temperature and avoidance of instabilities, since the chained networks gradually take the prediction of their predecessors in the chain as an extra input. It is observed that instabilities might occur in the iterative case, which does not happen with the chaining approach. To select relevant inputs and decrease the number of weights in this approach, Automatic Relevance Determination (ARD) for multilayer perceptrons is applied.

scholarly journals An exploratory study on the aerosol height retrieval from OMI measurements of the 477  nm O<sub>2</sub> − O<sub>2</sub> spectral band using a neural network approach

2017 ◽  
Vol 10 (3) ◽  
pp. 783-809 ◽  
Author(s):  
Julien Chimot ◽  
J. Pepijn Veefkind ◽  
Tim Vlemmix ◽  
Johan F. de Haan ◽  
Vassilis Amiridis ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Optical Thickness ◽  
Exploratory Study ◽  
Surface Albedo ◽  
Spectral Band ◽  
Single Scattering ◽  
Aerosol Layer ◽  
Layer Height

Abstract. This paper presents an exploratory study on the aerosol layer height (ALH) retrieval from the OMI 477 nm O2 − O2 spectral band. We have developed algorithms based on the multilayer perceptron (MLP) neural network (NN) approach and applied them to 3-year (2005–2007) OMI cloud-free scenes over north-east Asia, collocated with MODIS Aqua aerosol product. In addition to the importance of aerosol altitude for climate and air quality objectives, our long-term motivation is to evaluate the possibility of retrieving ALH for potential future improvements of trace gas retrievals (e.g. NO2, HCHO, SO2) from UV–visible air quality satellite measurements over scenes including high aerosol concentrations. This study presents a first step of this long-term objective and evaluates, from a statistic point of view, an ensemble of OMI ALH retrievals over a long time period of 3 years covering a large industrialized continental region. This ALH retrieval relies on the analysis of the O2 − O2 slant column density (SCD) and requires an accurate knowledge of the aerosol optical thickness, τ. Using MODIS Aqua τ(550 nm) as a prior information, absolute seasonal differences between the LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS) and average OMI ALH, over scenes with MODIS τ(550 nm) ≥ 1. 0, are in the range of 260–800 m (assuming single scattering albedo ω0 = 0. 95) and 180–310 m (assuming ω0 = 0. 9). OMI ALH retrievals depend on the assumed aerosol single scattering albedo (sensitivity up to 660 m) and the chosen surface albedo (variation less than 200 m between OMLER and MODIS black-sky albedo). Scenes with τ ≤ 0. 5 are expected to show too large biases due to the little impact of particles on the O2 − O2 SCD changes. In addition, NN algorithms also enable aerosol optical thickness retrieval by exploring the OMI reflectance in the continuum. Comparisons with collocated MODIS Aqua show agreements between −0. 02  ±  0. 45 and −0. 18  ±  0. 24, depending on the season. Improvements may be obtained from a better knowledge of the surface albedo and higher accuracy of the aerosol model. Following the previous work over ocean of Park et al.(2016), our study shows the first encouraging aerosol layer height retrieval results over land from satellite observations of the 477 nm O2 − O2 absorption spectral band.

Low-Carbon Russia: Prospects after the Crisis

2009 ◽  
pp. 107-120 ◽  
Author(s):  
I. Bashmakov
Keyword(s):  
Economic Growth ◽  
Potential Energy ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Low Carbon ◽  
Modeling Tools ◽  
Gas Emissions ◽  
Scenario Approach

On the eve of the worldwide negotiations of a new climate agreement in December 2009 in Copenhagen it is important to clearly understand what Russia can do to mitigate energy-related greenhouse gas emissions in the medium (until 2020) and in the long term (until 2050). The paper investigates this issue using modeling tools and scenario approach. It concludes that transition to the "Low-Carbon Russia" scenarios must be accomplished in 2020—2030 or sooner, not only to mitigate emissions, but to block potential energy shortages and its costliness which can hinder economic growth.

scholarly journals Biogeochemical Controls on the Redox Evolution of Earth’s Oceans and Atmosphere

Elements ◽  
2020 ◽  
Vol 16 (3) ◽  
pp. 191-196 ◽  
Author(s):  
Christopher T. Reinhard ◽  
Noah J. Planavsky
Keyword(s):  
Redox State ◽  
Modeling Tools ◽  
Evolutionary Trajectory ◽  
Geochemical Tracers ◽  
Atmosphere System ◽  
Ocean Atmosphere ◽  
Dramatic Shift ◽  
Redox Evolution

The redox state of Earth’s atmosphere has undergone a dramatic shift over geologic time from reducing to strongly oxidizing, and this shift has been coupled with changes in ocean redox structure and the size and activity of Earth’s biosphere. Delineating this evolutionary trajectory remains a major problem in Earth system science. Significant insights have emerged through the application of redox-sensitive geochemical systems. Existing and emerging biogeochemical modeling tools are pushing the limits of the quantitative constraints on ocean–atmosphere redox that can be extracted from geochemical tracers. This work is honing our understanding of the central role of Earth’s biosphere in shaping the long-term redox evolution of the ocean–atmosphere system.

Budapest airport air quality long-term studies by remote sensing with DOAS and FTIR with focus upon runway emissions

2007 ◽  
Author(s):  
Klaus Schäfer ◽  
Gregor Schürmann ◽  
Carsten Jahn ◽  
Candy Matuse ◽  
Herbert Hoffmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Long Term Studies
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