Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models

Capilla, Carmen

doi:10.2495/sdp-v11-n4-558-565

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Moreover, tropospheric ozone gives negative impact toward crops production and vegetation in general (Antanasijević et al, 2019); (Ismail et al, 2011). In Malaysia, regulations have been set by Various methods have been initiated for predicting the ozone concentration all over the world such as deterministic (theoretical and detailed atmospheric diffusion model) and statistical regression (Capilla, 2016;Gao et al, 2018;Hoshyaripour et al, 2016). Some methods have been developed based on linear regression concept.…”

Section: Introductionmentioning

confidence: 99%

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Jumin

Zaini

Ahmed

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R 2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.

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Section: Introductionmentioning

confidence: 99%

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Jumin

Zaini

Ahmed

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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“…XGBoost and Random Forest scored the highest for winter (R 2 = 0.75), while BD-LSTM was best in the summer and post-monsoon seasons (R 2 = 0.77 and 0.82). In previous research, the highest R 2 values for ozone have similarly been achieved with non-linear machine learning methods, including R 2 = 0.49 with Neural Power Networking [40]; R 2 = 0.72 with Random Forest [41]; R 2 = 0.84 with Boosted Decision Tree Regression [14]; and R 2 = 0.66 with a Multi-Layer Perceptron network [31]. Of course, these studies are not directly comparable because they were carried out at different locations with different data sets.…”

Section: Discussionmentioning

confidence: 74%

“…Elkamel et al [12] compared Artificial Neural Networks to both non-linear and linear regression models to predict current ozone levels based on meteorological conditions and precursor concentrations for a period of 60 days in Kuwait. Capilla [31] predicted ozone 1, 8, and 24 h in advance in an urban area on the eastern coast of the Iberian Peninsula and compared multiple linear regression with a Multi-Layer Perceptron network. Aljanabi et al [13] predicted ozone in Amman, Jordan, one day in advance, comparing a Multi-Layer Perceptron neural network (MLP), SVM, DTR, and the XGBoost algorithm, and found that MLP performed the best.…”

Section: Introductionmentioning

confidence: 99%

“…Eight machine learning methods were analyzed and tuned for optimal performance in this study, as compared to 2-3 methods in most other studies, offering more robust analyses for comparing models for real-world implementation [12,26,31,41]. For ozone prediction in Delhi in particular, this work provides a useful comparison with previous work [26,40,41].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Juarez¹,

Petersen

2021

Atmosphere

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Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

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“…Duenas et al [1] propose ARIMA models for estimating the ground-level ozone concentrations in air at an urban and rural sampling points in Southeastern Spain, [2] analyzes hourly ozone concentrations with multiple regression and multilayer perceptron models on observations of an urban area of the east coast of the Iberian Peninsula and [3] use ARIMA models for forecasting daily maximum surface ozone concentrations at the airport in Brunei Darussalam. A neural network approach has been used by [4][5][6][7][8] for predicting the intraday ozone concentrations.…”

Section: Introductionmentioning

confidence: 99%

Predicting hourly ozone concentrations using wavelets and ARIMA models

Salazar

Nicolis

Ruggeri

et al. 2018

Neural Comput & Applic

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In recent years, air pollution has been a major concern for its implications on human health. Specifically, ozone (O 3 ) pollution is causing common respiratory diseases. In this paper, we illustrate the process of modeling and prediction hourly O 3 pollution measurements using wavelet transforms. We split the time series of O 3 in daily intervals and estimate scale and wavelet coefficients for each interval by the discrete wavelet transform (DWT) with Haar filter. Subsequently we apply cumulated autoregressive integrated moving average (ARIMA) to estimate the coefficients and forecast their evolution in future intervals. Then the inverse discrete wavelet transform is implemented for the reconstruction of the time series and the forecast in the near future. In order to assess the performance of the proposed methodology, we compare the predictions obtained by the DWT-ARIMA with those obtained by the ARIMA model. Several theoretical results are shown through a simulation study.

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Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models

Cited by 11 publications

References 8 publications

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Predicting hourly ozone concentrations using wavelets and ARIMA models

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