Hourly Air Quality Index (AQI) Forecasting Using Machine Learning Methods

2020 ◽  
pp. 123-132
Author(s):  
Jose Antonio Moscoso-López ◽  
Daniel Urda ◽  
Javier González-Enrique ◽  
Juan Jesus Ruiz-Aguilar ◽  
Ignacio J. Turias
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Learning Methods ◽  
Machine Learning Methods

Comparison of Different Machine Learning Methods to Forecast Air Quality Index

2019 ◽  
pp. 235-245
Author(s):  
Bo Liu ◽  
Chao Shi ◽  
Jianqiang Li ◽  
Yong Li ◽  
Jianlei Lang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Learning Methods ◽  
Machine Learning Methods

Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods

2016 ◽  
Vol 10 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Huiping Peng ◽  
Aranildo R. Lima ◽  
Andrew Teakles ◽  
Jian Jin ◽  
Alex J. Cannon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Air Quality Forecasting

scholarly journals Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

2019 ◽  
Vol 9 (19) ◽  
pp. 4069 ◽  
Author(s):  
Huixiang Liu ◽  
Qing Li ◽  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

scholarly journals Prediction and Forecasting of Air Quality Index in Chennai using Regression and ARIMA time series models

2021 ◽  
Vol 9 ◽  
Author(s):  
Geetha Mani ◽  
◽  
Joshi Kumar Viswanadhapalli ◽  
Albert Alexander Stonie ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Air Quality ◽  
Linear Regression ◽  
Quality Index ◽  
Air Quality Index ◽  
Model Parameters ◽  
Sensor Output ◽  
Model Accuracy ◽  
Life On Earth

Air is one of the most fundamental constituents for the sustenance of life on earth. The meteorological, traffic factors, consumption of non-renewable energy sources, and industrial parameters are steadily increasing air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of air quality in our environment needs to be monitored continuously. The Air Quality Index (AQI), which indicates air quality, is influenced by several individual factors such as the accumulation of NO2, CO, O3, PM2.5, SO2, and PM10. This research paper aims to predict and forecast the AQI with Machine Learning (ML) techniques, namely linear regression and time series analysis. Primarily,Multi Linear Regression (MLR) model, supervised machine learning, is developed to predict AQI. NO2, Ozone(O3), PM 2.5, and SO2 sensor output collected from Central Pollution Control Board (CPCB) – Chennai region, India feed as input features and optimized AQI calculated from sensor's output set as a target to train the regression model. The obtained model parameters are validated with new and unseen sensor output. The Key Performance Indices(KPI) like co-efficient of determination, root mean square error and mean absolute error were calculated to validate the model accuracy. The K-cross-fold validation for testing data of MLR was obtained as around 92%. Secondly, the Auto-Regressive Integrated Moving Average (ARIMA) time series model is applied to forecast the AQI. The obtained model parameters were validated with unseen data with a timestamp. The forecasted AQI value of the next 15 days lies in a 95 % confidence interval zone. The model accuracy of test data was obtained as more than 80%.

scholarly journals AQ-Bench: a benchmark dataset for machine learning on global air quality metrics

2021 ◽  
Vol 13 (6) ◽  
pp. 3013-3033
Author(s):  
Clara Betancourt ◽  
Timo Stomberg ◽  
Ribana Roscher ◽  
Martin G. Schultz ◽  
Scarlet Stadtler
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Tropospheric Ozone ◽  
Environmental Science ◽  
Quality Metrics ◽  
Quality Data ◽  
Easy Access ◽  
Linear Regression Method ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.

scholarly journals AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics

2021 ◽  
Author(s):  
Clara Betancourt ◽  
Timo Stomberg ◽  
Scarlet Stadtler ◽  
Ribana Roscher ◽  
Martin G. Schultz
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Tropospheric Ozone ◽  
Environmental Science ◽  
Quality Metrics ◽  
Quality Data ◽  
Easy Access ◽  
Linear Regression Method ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. We validate these data as a machine learning benchmark by providing a well-defined task together with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/toar/ozone-mapping . AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.

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