OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

Lautenschlager, Florian; Becker, Martin; Kobs, Konstantin; Steininger, Michael; Davidson, Padraig; Krause, Anna; Hotho, Andreas

doi:10.1016/j.atmosenv.2020.117535

Cited by 23 publications

(10 citation statements)

References 39 publications

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“…In addition, we found that the ML-K approach ran faster than the well-established models. The benefits of using MLbased models are also identified in other studies, including a minimized manual effort for predictor variables, 14 improved prediction accuracy, 9,31,56 and suitability for predicting air pollution for large geographies. 30,57 One could imagine testing our off-the-shelf ML-based approach to facilitate LUR model improvement by including a variety of new microscale variables from open data sources for different geographies.…”

Section: Lur Modelmentioning

confidence: 93%

“…12 Variables assembled using data developed by administrative sources may miss local information, 13 making it difficult to generalize LUR models across regions. 14 Standardized land use (e.g., gridbased land cover data) and traffic data developed by federal agencies may capture regional air pollution emission sources but likely do not capture many local emission sources or features that modify air pollution dispersion. 15 This is partly because the process by which they are developed is slow, and they serve administrative needs that do not necessarily align with goals of air-quality modeling.…”

Section: Introductionmentioning

confidence: 99%

“…For example, variables tabulated within Census geographies − may suffer from issues like the modifiable areal unit problem . Variables assembled using data developed by administrative sources may miss local information, making it difficult to generalize LUR models across regions . Standardized land use (e.g., grid-based land cover data) and traffic data developed by federal agencies may capture regional air pollution emission sources but likely do not capture many local emission sources or features that modify air pollution dispersion .…”

Section: Introductionmentioning

confidence: 99%

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National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

Marshall

Zhang

et al. 2021

Environ. Sci. Technol.

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National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO 2 , PM 2.5 , O 3 , CO, PM 10 , SO 2 ) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10fold spatial cross-validation (CV) R 2 increased 0.02−0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R 2 = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

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Section: Lur Modelmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

Marshall

Zhang

et al. 2021

Environ. Sci. Technol.

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“…Secondly, the deep neural network algorithm was applied on the LUR results to fit the model for predicting concentrations. Lautenschlager et al [33] focus on the development of the OpenLUR platform. This platform consists of the LUR modelling technique combined with machine learning and open datasets.…”

Section: Neural Network-based Regressionmentioning

confidence: 99%

The Neural Network Assisted Land Use Regression

2021

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Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.

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“…Typical tasks that are performed with the models implemented with AutoML are classification tasks. For example, it was used in the health sector [28,30,31], the corporate sector [32], the environmental sector [33,34], the energy sector [35,36], and others [37][38][39]. There are many AutoML tools and solutions available today to help data scientists.…”

Section: Automated Machine Learningmentioning

confidence: 99%

Using a DEA–AutoML Approach to Track SDG Achievements

Singpai

2020

Sustainability

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Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.

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OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

Cited by 23 publications

References 39 publications

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

The Neural Network Assisted Land Use Regression

Using a DEA–AutoML Approach to Track SDG Achievements

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