Urban Air Quality Forecasting: A Regression and a Classification Approach

Karatzas, Kostas; Katsifarakis, Nikos; Orłowski, Cezary; Sarzynski, Arkadiusz

doi:10.1007/978-3-319-54430-4_52

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…The RF algorithm can be ranked as 2nd, achieving correlation coefficients very close to the ones received with the aid of LR (and surpassing it for the Eptapyrgiou station), while in some cases leading to the best (lower) MAE (like in the AM3, Martiou and Eptapyrgiou stations) and to the best (lower) RMSE (like in the AM3 and in the Eptapyrgiou stations). LR is a simple algorithm of linear logic generally considered weak in depicting nonlinear phenomena like the ones involved in AQ problems, and usually performing more poorly when compared with algorithms like ANNs or RF [1]. The success of the specific algorithm in our case has to do with the limited number of atmospheric quality parameters being available in all studied areas and stations (low number of features), thus leading to the (possible) exclusion of nonlinear dependencies from the available dataset, and dictating persistence as the main mechanism affecting the forecast of PM 10 levels one day in advance [26].…”

Section: Resultsmentioning

confidence: 99%

“…where J T is the (transposed) Jacobian and e(t) is the overall error vector [1]. In this specific case a MultiLayer Perceptron Network with a feed-forward architecture and a back propagation training method was used, with an input layer consisting 5 nodes (i.e.…”

Section: Algorithms For Single Station Model Creationmentioning

confidence: 99%

“…(iii) Random Forests (RF), an ensemble method originating from the Decision Tree family of algorithms [24] that has shown high capacity to effectively model atmospheric parameters of interest [1]. The method creates N subsets of the input vector x using random selection with replacement, each subset containing 2/3 of the initial data, while the remaining data are used to estimate error and variable importance.…”

Section: Algorithms For Single Station Model Creationmentioning

confidence: 99%

“…In a recently published paper [1] we underlined the importance of air quality (AQ) forecasting in urban environmental management as well as in contemporary smart city development [2,3]. In the current paper we revisit and extend our initial approach, focusing on urban AQ forecasting from the regression point of view and incorporating an ensemble modelling approach.…”

Section: Introductionmentioning

confidence: 99%

“…The research question posed in the current paper moves one step ahead of our previously published results [1] and addresses (a) the ability of a low dimensionality feature space (small number of input parameters) to support effective datadriven models for PM 10 forecasting and (b) the modelling approach to be used in terms of algorithms and their setup (single vs. ensemble oriented models). In addition, we make use of an ensemble approach based on an ANN model of simple architecture which can be applied to multiple geographic areas, thus simplifying the ensemble approach suggested by [14] and [15], while maintaining a performance comparable to the one reported by similar studies [16], and therefore providing with a novel approach to the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

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Revisiting urban air quality forecasting: a regression approach

Karatzas

Katsifarakis

Orłowski

et al. 2018

Vietnam J Comput Sci

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We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean PM 10 concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.

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Section: Resultsmentioning

confidence: 99%

Section: Algorithms For Single Station Model Creationmentioning

confidence: 99%

Section: Algorithms For Single Station Model Creationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Revisiting urban air quality forecasting: a regression approach

Karatzas

Katsifarakis

Orłowski

et al. 2018

Vietnam J Comput Sci

Self Cite

View full text Add to dashboard Cite

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A Multiple Kernel Learning Approach for Air Quality Prediction

Zheng

et al. 2018

Advances in Meteorology

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Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). e centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

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Beşi̇ktaş’taki̇ Hava Kali̇tesi̇ İndeksi̇ni̇n Yapay Si̇ni̇r Ağlari Ve K en Yakin Komşuluk Algori̇tmalari İle Tahmi̇ni̇

Baran

2021

Mühendislik Bilimleri Ve Tasarım Dergisi

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In this study, Air Quality Index (AQI) in Besiktas was intended to be predicted by Artificial Neural Networks (ANN) and k Nearest Neighbors (kNN) algorithms. For this purpose, eight parameters have been selected which may affect the AQI. These parameters are PM10, SO2, CO, O3, temperature, humidity, pressure and wind speed, respectively. 124 data for 2015, 2016, 2017 and 2018 January, which includes eight parameters, were determined as training data. The first 14-day data of January 2019 were determined as test data. Similarly, the first 14-day data of January, March and December of 2018 were used as test data. In addition, The first 14-day data for January 2019 were normalized and set as test data. The success of ANN and kNN were measured by comparing.

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Urban Air Quality Forecasting: A Regression and a Classification Approach

Cited by 3 publications

References 7 publications

Revisiting urban air quality forecasting: a regression approach

Revisiting urban air quality forecasting: a regression approach

A Multiple Kernel Learning Approach for Air Quality Prediction

Beşi̇ktaş’taki̇ Hava Kali̇tesi̇ İndeksi̇ni̇n Yapay Si̇ni̇r Ağlari Ve K en Yakin Komşuluk Algori̇tmalari İle Tahmi̇ni̇

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