HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors

Hu, Ke; Rahman, Ashfaqur; Bhrugubanda, Hari; Sivaraman, Vijay

doi:10.1109/jsen.2017.2690975

Cited by 80 publications

(29 citation statements)

References 21 publications

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“…Machine learning, as a powerful modeling tool, has successfully improved the estimation and classification accuracy of environmental variables (air pollution, vegetation health condition, soil moisture, land surface temperature, etc.) and land cover types from remotely sensed images [26][27][28][29][30][31][32]. In addition, machine learning algorithms are excellent in solving nonlinear problems of variables with very high dimensions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

et al. 2018

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Hyperspectral sensors provide detailed information for dust retention content (DRC) estimation. However, rich hyperspectral data are not fully utilized by traditional image analysis techniques. We integrated several recently developed machine learning algorithms to estimate DRC on plant leaves using the spectra measured by the ASD FieldSpec 3. The experiments were carried out on three common green plants of southern China. The important hyperspectral variables were first identified by applying the random forest (RF) algorithm. Three estimation models were then developed using the support vector machine (SVM), classification and regression tree (CART), and RF algorithms. The results showed that the increase in dust retention contents on plant leaves enhanced their reflectance in the visible wavelength but weakened their reflectance in the infrared wavelength. Wavelengths in the ranges of 450–500 nm, 550–600 nm, 750–1000 nm, and 1100–1300 nm were identified as important variables using the RF algorithm and were used to estimate the DRC. The comparison of the three machine learning techniques for DRC estimation confirmed that the SVM and RF models performed well because their estimations were similar to the measured DRC. Specifically, the average R2 for SVM and RF model are 0.85 and 0.88. The technical approach of this study proved to be a successful illustration of using hyperspectral measurements to estimate the DRC on plant leaves. The findings of this study can be applied to monitor the DRC on leaves of other plants and can also be integrated with other types of spectral data to measure the DRC at a regional scale.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

et al. 2018

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“…No previous studies on applying LES air pollution data to train a machine learning model exist and therefore direct comparison is unfeasible. Also comparing to studies applying spatial air quality measurements (Adams and Kanaroglou, 2016;Hu et al, 2017;Krecl et al, 2019;Van den Bossche et al, 2018) is difficult, as the spatial resolution of the training data is of the lower order of magnitude. Still some linkage can be found.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

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Kurppa

et al. 2020

Preprint

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Abstract. Running large-eddy simulations (LES) can be burdensome and computationally too expensive from the application point-of-view for example to support urban planning. In this study, regression models are used to replicate modelled air pollutant concentrations from LES in urban boulevards. We study the performance of regression models and discuss how to detect situations where the models are applied outside their training domain and their outputs cannot be trusted. Regression models from 10 different model families are trained and a cross-validation methodology is used to evaluate their performance and to find the best set of features needed to reproduce the LES outputs. We also test the regression models on an independent testing dataset. Our results suggest that in general, log-linear regression gives the best and most robust performance on new independent data. It clearly outperforms the dummy model which would predict constant concentrations for all locations (mRMSE of 0.76 vs 1.78 of the dummy model). Furthermore, we demonstrate that it is possible to detect concept drift, i.e., situations where the model is applied outside its training domain and a new LES run may be necessary to obtain reliable results. Regression models can be used to replace LES simulations in estimating air pollutant concentrations, unless higher accuracy is needed. In order to have reliable results, it is however important to do the model and feature selection carefully to avoid over-fitting and to use methods to detect the concept drift.

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“…The results show that the method is able to predict PM 2.5 by using data provided by sparse monitoring stations. Another technique consists of supplementing sparse accurate station monitoring by lower fidelity dense mobile sensors, with the aim of getting a fine spatial granularity [54]. Seven Regression models are used to predict CO concentrations in Sidney, Australia.…”

Section: Category 2: Image-based Monitoring and Tackling Low Spatial mentioning

confidence: 99%

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

2018

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Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

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HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors

Cited by 80 publications

References 21 publications

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

Machine Learning for Estimating Leaf Dust Retention Based on Hyperspectral Measurements

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

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