Ensemble method based on Artificial Neural Networks to estimate air pollution health risks

Araujo, Lilian N.; Belotti, Jônatas Trabuco; Alves, Thiago Antonini; Tadano, Yara de Souza; Siqueira, Hugo Valadares

doi:10.1016/j.envsoft.2019.104567

Cited by 71 publications

(64 citation statements)

References 29 publications

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“…We followed the premises from the literature of adopting the MSE as the most important error metric because this is reduced during the training (adjustment) of the neural models ( Araujo et al, 2020 ; Kachba et al, 2020 ; Siqueira et al, 2014 , 2018 , 2020 bib_Siqueira_et_al_2020 bib_Siqueira_et_al_2018 bib_Siqueira_et_al_2014 ).…”

Section: Methodsmentioning

confidence: 99%

“…Artificial Neural Networks (ANN), on the other hand, is a nonlinear methodology capable of mapping a set of inputs into an output, which is important to support decisions regarding preventive measures. This approach has been used in air pollution epidemiological studies ( Araujo et al, 2020 ; Kachba et al, 2020 ; Kassomenos et al, 2011 ; Polezer et al, 2018 ). In Araujo et al (2020) and Kassomenos et al (2011) , the ANN showed a better performance than linear approaches as Generalized Linear Models.…”

Section: Introductionmentioning

confidence: 99%

“…This approach has been used in air pollution epidemiological studies ( Araujo et al, 2020 ; Kachba et al, 2020 ; Kassomenos et al, 2011 ; Polezer et al, 2018 ). In Araujo et al (2020) and Kassomenos et al (2011) , the ANN showed a better performance than linear approaches as Generalized Linear Models. Kassomenos et al (2011) also concluded that ANN is a more flexible and adaptive mathematical approach.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Tadano

Potgieter‐Vermaak

Kachba

et al. 2021

Environmental Pollution

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Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide due to global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O 3 , NO 2 , NO, PM 2.5 , and PM 10 , for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Tadano

Potgieter‐Vermaak

Kachba

et al. 2021

Environmental Pollution

Self Cite

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“…Unfortunately, there is no consensus regarding what is the best approach to estimate air pollution health risks [16,20,25,26,41], as it depends on the database behavior. Even for conventional regressions, such as the Generalized Linear Model, the best distribution may depend on the dataset, as reported by Ardiles et al [58].…”

Section: Case Studymentioning

confidence: 99%

“…Polezer et al [20] showed that ANN can be applied to forecast air pollution impact on human health, even when the dataset is short or has many gaps (a Brazilian data reality). Araujo et al [41] used ten distinct ANN, four ensembles and Generalized Linear Models (GLM) to estimate PM 10 impact on hospital admissions for respiratory diseases in Campinas and São Paulo cities, Brazil, concluding that ANN and ensembles had better performances than GLM.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

et al. 2020

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The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

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An illustration of model agnostic explainability methods applied to environmental data

et al. 2022

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Historically, two primary criticisms statisticians have of machine learning and deep neural models is their lack of uncertainty quantification and the inability to do inference (i.e., to explain what inputs are important). Explainable AI has developed in the last few years as a sub‐discipline of computer science and machine learning to mitigate these concerns (as well as concerns of fairness and transparency in deep modeling). In this article, our focus is on explaining which inputs are important in models for predicting environmental data. In particular, we focus on three general methods for explainability that are model agnostic and thus applicable across a breadth of models without internal explainability: “feature shuffling”, “interpretable local surrogates”, and “occlusion analysis”. We describe particular implementations of each of these and illustrate their use with a variety of models, all applied to the problem of long‐lead forecasting monthly soil moisture in the North American corn belt given sea surface temperature anomalies in the Pacific Ocean.

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Ensemble method based on Artificial Neural Networks to estimate air pollution health risks

Cited by 71 publications

References 29 publications

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

An illustration of model agnostic explainability methods applied to environmental data

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