A comparison of ranking filter methods applied to the estimation of NO2 concentrations in the Bay of Algeciras (Spain)

González-Enrique, Javier; Ruiz-Aguilar, Juan Jesús; Moscoso-López, José Antonio; Urda, Daniel; Turias, Ignacio J.

doi:10.1007/s00477-021-01992-4

Cited by 14 publications

(5 citation statements)

References 52 publications

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“…A resampling technique has been employed to mitigate test set prediction errors and counteract overfitting. Performance results were exclusively gathered for the test set to estimate the generalisation error of each model using unseen data, following a methodology successfully implemented in prior studies [30,31]. We implemented a twofold cross-validation technique to identify the optimal model, considering its generalisation performance.…”

Section: Methodsmentioning

confidence: 99%

“…This work is framed in a similar line to the aforementioned studies. Previous work has focused on the prediction of air pollution using machine learning and meteorological data such as in [30][31][32], highlighting the latest studies based on deep learning techniques in [30,31]. In [31], the study focuses on predicting maritime traffic-related pollutant concentrations in the Bay of Algeciras, Spain.…”

Section: Introductionmentioning

confidence: 99%

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Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Rodríguez-García,

Carrasco-García,

Ribeiro

et al. 2024

JMSE

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Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Rodríguez-García,

Carrasco-García,

Ribeiro

et al. 2024

JMSE

Self Cite

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“…The mRMR method is a typical spatial search-based filtering method proposed by Peng et al in 2005, which uses mutual information to measure the relevance and redundancy of features (González-Enrique et al 2021 ). Let be the set of influencing factor features, we need to select meteorological features with high correlation with PM 2.5 from influencing factors.…”

Section: Methodsmentioning

confidence: 99%

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

Wang

Dong

et al. 2022

Stoch Environ Res Risk Assess

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Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM 2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM 2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.

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“…Finally, an imputation procedure based on ANNs was applied in order to fill in missing values. This procedure was developed by the authors in a previous work and used by them in [46][47][48][49][50]53]. Each variable is estimated using a different ANN trained using available data.…”

Section: Ms1 Algeciras Ms2-ms16mentioning

confidence: 99%

Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)

et al. 2023

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Predicting air quality is a very important task, as it is known to have a significant impact on health. The Bay of Algeciras (Spain) is a highly industrialised area with one of the largest superports in Europe. During the period 2017–2019, different data were recorded in the monitoring stations of the bay, forming a database of 131 variables (air pollutants, meteorological information, and vessel data), which were predicted in the Algeciras station using long short-term memory models. Four different approaches have been developed to make SO2 and NO2 forecasts 1 h and 4 h in Algeciras. The first uses the remaining 130 exogenous variables. The second uses only the time series data without exogenous variables. The third approach consists of using an autoregressive time series arrangement as input, and the fourth one is similar, using the time series together with wind and ship data. The results showed that SO2 is better predicted with autoregressive information and NO2 is better predicted with ships and wind autoregressive time series, indicating that NO2 is closely related to combustion engines and can be better predicted. The interest of this study is based on the fact that it can serve as a resource for making informed decisions for authorities, companies, and citizens alike.

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A comparison of ranking filter methods applied to the estimation of NO2 concentrations in the Bay of Algeciras (Spain)

Cited by 14 publications

References 52 publications

Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)

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