Prediction of PM10 concentrations in the city of Agadir (Morocco) using non-linear autoregressive artificial neural networks with exogenous inputs (NARX)

Adnane, Anas; Leghrib, Radouane; Chaoufi, Jamal; Chirmata, Ahmed

doi:10.1016/j.matpr.2021.11.340

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…In the current study, a non-linear autoregressive model of deep learning was used for PM10 time series forecasting task. The main feature of this method is that it accounts for seasonality and other temporal structures like trends to handle the autocorrelation rooted in the data [26]. Figure 3 shows the basics of non-linear autoregressive model.…”

Section: Methodsmentioning

confidence: 99%

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey

Batur,

Babii

2024

IOP Conf. Ser.: Earth Environ. Sci.

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The main purpose of this study is to select the most reliable nonlinear computational model to predict the particulate matter (PM10) concentrations. Time series data of three years PM10 concentrations were used as input variable. For the prediction, three different types of dynamic nonlinear autoregressive models were built and compared. These models are the Levenberg-Marquardt algorithm, the Bayesian Regulization algorithm, and the Scaled Conjugate Gradient algorithm. For each of these algorythms, various settings were adopted with the subsequent statistical analysis. To analyse the model performance, we used mean prediction error, root mean square error, and correlation coefficient. The lowest root mean square errors were found for the Levenberg-Marquardt algorithm with 15 neurons, for the Bayesian Regularization and for the Scaled Conjugate Gradient algorithm with 20 neurons in hidden layer. In our study, we focused on the long-term forecast of stationary dynamic time series data and on the large amount of data, which is presented as a scientific novelty. Additionally, we determined the main model parameters that most improve quality in terms of training and network capacity. Therefore, the derived forecasting model can be used as a priori for air quality management and regulations aimed on the reducing of pollutant level.

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Section: Methodsmentioning

confidence: 99%

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey

Batur,

Babii

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Although many studies have attempted to predict particulate matter using various tools (Agarwal & Sahu, 2023;Folifack Signing et al, 2024;Gul et al, 2022;Khan et al, 2022;Masood & Ahmad, 2023;Rahman & Kabir, 2023;Verma et al, 2023), research specifically predicting future levels of 𝑃𝑀 10 in Morocco remains limited. Some studies limit their use to a single station where the models were developed, producing only one forecast for the next hour or the next day (Adnane et al, 2022;Bouakline et al, 2022), or they were only validated at a single station, which limits the generalization of their models to other areas Ajdour, Leghrib, Chaoufi, Fetmaoui, et al, 2020;Saidi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Predicting Particulate Matter (PM10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Houdou,

Khomsi,

Monache

et al. 2024

Preprint

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Accurate prediction of Particulate Matter (\({PM}_{10}\)) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of \({PM}_{10}\) over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process \({PM}_{10}\) forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 \(\mu g/{m}^{3}\) in the original forecasts to 44.73 \(\mu g/{m}^{3}\) with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 \(\mu g/{m}^{3}\) to 24.30 \(\mu g/{m}^{3}\). Additionally, the coefficient of determination (\({R}^{2}\)) increased more than twofold from 29.11–65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of \({PM}_{10}\) pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.

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“…According to Elaine Lui [4], in recent years, forecast models of particle matter have been proposed as a helpful tool for the management of air quality in several cities around the world. There are many deterministic models to assess and predict the dispersion of pollutants in urban areas; however, most of them are causal and therefore fail to predict extreme concentrations [15]. Artificial intelligence models, such as neural networks, have already proven to be efficient in several areas and have shown excellent results in the modelling and prediction of time series [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Prediction of Particulate Matter (PM2.5 and PM10) around the Ipbeja Campus

et al. 2022

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Nowadays, most of the world’s population lives in urban centres, where air quality levels are not strictly checked; citizens are exposed to air quality levels over the limits of the World Health Organization. The interaction between the issuing and atmospheric sources influences the air quality or level. The local climate conditions (temperature, humidity, winds, rainfall) determine a greater or less dispersion of the pollutants present in the atmosphere. In this sense, this work aimed to build a math modelling prediction to control the air quality around the campus of IPBeja, which is in the vicinity of a car traffic zone. The researchers have been analysing the data from the last months, particle matter (PM10 and PM2.5), and meteorological parameters for prediction using NARX. The results show a considerable increase in particles in occasional periods, reaching average values of 135 μg/m3 for PM10 and 52 μg/m3 for PM2.5. Thus, the monitoring and prediction serve as a warning to perceive these changes and be able to relate them to natural phenomena or issuing sources in specific cases.

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Prediction of PM10 concentrations in the city of Agadir (Morocco) using non-linear autoregressive artificial neural networks with exogenous inputs (NARX)

Cited by 8 publications

References 19 publications

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey

Predicting Particulate Matter (PM10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Monitoring and Prediction of Particulate Matter (PM2.5 and PM10) around the Ipbeja Campus

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Prediction of PM10 concentrations in the city of Agadir (Morocco) using non-linear autoregressive artificial neural networks with exogenous inputs (NARX)

Cited by 8 publications

References 19 publications

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM10) in the eastern part of Turkey

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM10) in the eastern part of Turkey

Predicting Particulate Matter (PM10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Monitoring and Prediction of Particulate Matter (PM2.5 and PM10) around the Ipbeja Campus

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The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey

The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey