Prediction of Pollutant Concentrations by Meteorological Data Using Machine Learning Algorithms

Alpan, Kezban; Şekeroğlu, Boran

doi:10.5194/isprs-archives-xliv-4-w3-2020-21-2020

Cited by 8 publications

(3 citation statements)

References 13 publications

(6 reference statements)

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“…Next, the results also show that random forest is the best-proposed model for predicting SO 2 , NO 2 , O 3 , and CO concentrations for the next day since it has the lowest error and highest accuracy, with a total of rank values of 4 for NO 2 and 3 for SO 2 , O 3 , and CO. These results are similar to Alpan and Sekeroglu (2020), where the RF model achieved the best results in predicting air pollution concentration compared to the other two models in their study.…”

Section: Prediction Modelsupporting

confidence: 86%

Prediction of Daily Air Pollutants Concentration and Air Pollutant Index Using Machine Learning Approach

Mustakim¹,

Ul-Saufie²,

Shaziayani³

et al. 2022

JST

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The major air pollutants in Malaysia that contribute to air pollution are carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter. Predicting the air pollutants concentration can help the government to monitor air quality and provide awareness to the public. Therefore, this study aims to overcome the problem by predicting the air pollutants concentration for the next day. This study focuses on an industrial, the Petaling Jaya monitoring station in Selangor. The data is obtained from the Department of Environment, which contains the dataset from 2004 to 2018. Subsequently, this study is conducted to construct predictive modeling that can predict the air pollutants concentrations for the next day using a tree-based approach. From the comparison of the three models, a random forest is a best-proposed model. The results of PM10 concentration prediction for the random forest is the best performance which is shown by RMSE (15.7611–19.0153), NAE (0.6508–0.8216), and R2 (0.346–0.5911). For SO2, the RMSE was 0.0016–0.0017, the NAE was 0.7056–0.8052, and the R2 was 0.3219–0.4676. The RMSE (0.0062–0.0075), the NAE (0.7892–0.9591), and the R2 (0.0814–0.3609) for NO2. The RMSE (0.3438–0.3975), NAE (0.7387–0.9015), and R2 (0.2005–0.4399) for CO were all within acceptable limits. For O3, the RMSE was 0.0051–0.0057, the NAE was 0.8386–0.9263, and the R2 was 0.1379–0.2953. The API calculation results indicate that PM10 is a significant pollutant in representing the API.

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Section: Prediction Modelsupporting

confidence: 86%

Prediction of Daily Air Pollutants Concentration and Air Pollutant Index Using Machine Learning Approach

Mustakim¹,

Ul-Saufie²,

Shaziayani³

et al. 2022

JST

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“…In their study, Alpan and Sekeroglu (2020) predicted six pollutant levels using machine learning algorithms with meteorological data such as precipitation and temperature. The random forest had a high prediction ability with experiments on two different datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Meteorological factors influence air quality. The studies Ameer et al (2019), Qin et al (2019), Alpan and Sekeroglu (2020)…”

Section: Related Workmentioning

confidence: 99%

Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data

Saminathan¹,

Malathy²

2023

Front. Big Data

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Air pollution is a serious challenge to humankind as it poses many health threats. It can be measured using the air quality index (AQI). Air pollution is the result of contamination of both outdoor and indoor environments. The AQI is being monitored by various institutions globally. The measured air quality data are kept mostly for public use. Using the previously calculated AQI values, the future values of AQI can be predicted, or the class/category value of the numeric value can be obtained. This forecast can be performed with more accuracy using supervised machine learning methods. In this study, multiple machine-learning approaches were used to classify PM2.5 values. The values for the pollutant PM2.5 were classified into different groups using machine learning algorithms such as logistic regression, support vector machines, random forest, extreme gradient boosting, and their grid search equivalents, along with the deep learning method multilayer perceptron. After performing multiclass classification using these algorithms, the parameters accuracy and per-class accuracy were used to compare the methods. As the dataset used was imbalanced, a SMOTE-based approach for balancing the dataset was used. Compared to all other classifiers that use the original dataset, the accuracy of the random forest multiclass classifier with SMOTE-based dataset balancing was found to provide better accuracy.

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Comparative Evaluation of Machine Learning Models for the Prediction of Diabetes at Early Stage

Ozkan¹,

Şekeroğlu²

2022

Artificial Intelligence and Sustainable Computing

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Prediction of Pollutant Concentrations by Meteorological Data Using Machine Learning Algorithms

Cited by 8 publications

References 13 publications

Prediction of Daily Air Pollutants Concentration and Air Pollutant Index Using Machine Learning Approach

Prediction of Daily Air Pollutants Concentration and Air Pollutant Index Using Machine Learning Approach

Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data

Comparative Evaluation of Machine Learning Models for the Prediction of Diabetes at Early Stage

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