Recursive neural network model for analysis and forecast of PM10 and PM2.5
TL;DR: In this article, the authors used three models: a multiple linear regression model, a neural network model with and without recursive architecture to forecast the daily averaged concentration of PM10 from one to three days ahead.
About: This article is published in Atmospheric Pollution Research.The article was published on 2017-07-01. It has received 228 citations till now. The article focuses on the topics: Recurrent neural network.
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TL;DR: A protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models, highlighting the need for developing systematic protocols for developing powerful ANN models.
Abstract: Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models.
217 citations
TL;DR: The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.
Abstract: This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at ai...
169 citations
Cites background or methods from "Recursive neural network model for ..."
...5 (Biancofiore et al. 2017)....
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...An RNN with one time step, or delay, is called an Elman network (EN) and has been used successfully to predict air quality in previous studies (Biancofiore et al. 2017, 2015)....
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...Many studies have used supervised machine learning techniques, such as artificial neural networks (ANNs) to predict O3 time-series concentrations (Biancofiore et al. 2017; Ettouney et al. 2009; Kurt et al. 2008; Wirtz et al. 2005)....
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TL;DR: A novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers is proposed and the conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.
Abstract: In recent years, the haze has caused serious troubles to people's lives, with the continuous increase of PM2.5 emissions. The accurate prediction of PM2.5 is very crucial for policy makers to make predictive measures. Due to the nonlinearity of the PM2.5 time series, it is difficult to predict accurately. Despite some studies about PM2.5 being proposed, the problem of the LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers isn't still solved. In this study, a novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM is proposed. Firstly, six study sites from China are taken as examples and WT is used to decompose PM2.5 time series into several low-and high- frequency components based on different samples. Secondly, the decomposed components are predicted based on SAE-LSTM. Finally, the predicted results are reconstructed in view of all low-and high-frequency components and the predicted results are obtained. The results imply that: (1) the forecasting performance of SAE-LSTM is better than that of other models (e.g., BP (back propagation)) used for comparison; (2) for six different PM 2.5 samples, four orders five layers, five orders six layers, five orders seven layers, three orders six layers, five orders seven layers, and five orders six layers are the most appropriate. The conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.
141 citations
TL;DR: The E-LSTM model inspired by ensemble learning, which constructs multiple LSTMs in different modes, obtained better forecasting performance than that using the single LSTM and feed forward neural network in terms of mean absolute percentage error.
130 citations
TL;DR: In this article, data mining algorithms were used to establish the most influential meteorological variables on air pollution in Bogota, and to develop models to forecast PM10 and PM2.5 to help local authorities prevent human exposure to high levels of pollution.
110 citations
References
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Book•
16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
29,130 citations
TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
10,264 citations
TL;DR: Fine particulate and sulfur oxide--related pollution were associated with all-cause, lung cancer, and cardiopulmonary mortality and long-term exposure to combustion-related fine particulate air pollution is an important environmental risk factor for cardiopULmonary and lung cancer mortality.
Abstract: ContextAssociations have been found between day-to-day particulate air pollution
and increased risk of various adverse health outcomes, including cardiopulmonary
mortality. However, studies of health effects of long-term particulate air
pollution have been less conclusive.ObjectiveTo assess the relationship between long-term exposure to fine particulate
air pollution and all-cause, lung cancer, and cardiopulmonary mortality.Design, Setting, and ParticipantsVital status and cause of death data were collected by the American
Cancer Society as part of the Cancer Prevention II study, an ongoing prospective
mortality study, which enrolled approximately 1.2 million adults in 1982.
Participants completed a questionnaire detailing individual risk factor data
(age, sex, race, weight, height, smoking history, education, marital status,
diet, alcohol consumption, and occupational exposures). The risk factor data
for approximately 500 000 adults were linked with air pollution data
for metropolitan areas throughout the United States and combined with vital
status and cause of death data through December 31, 1998.Main Outcome MeasureAll-cause, lung cancer, and cardiopulmonary mortality.ResultsFine particulate and sulfur oxide–related pollution were associated
with all-cause, lung cancer, and cardiopulmonary mortality. Each 10-µg/m3 elevation in fine particulate air pollution was associated with approximately
a 4%, 6%, and 8% increased risk of all-cause, cardiopulmonary, and lung cancer
mortality, respectively. Measures of coarse particle fraction and total suspended
particles were not consistently associated with mortality.ConclusionLong-term exposure to combustion-related fine particulate air pollution
is an important environmental risk factor for cardiopulmonary and lung cancer
mortality.
7,803 citations
TL;DR: It is suggested that fine-particulate air pollution, or a more complex pollution mixture associated with fine particulate matter, contributes to excess mortality in certain U.S. cities.
Abstract: Background Recent studies have reported associations between particulate air pollution and daily mortality rates. Population-based, cross-sectional studies of metropolitan areas in the United States have also found associations between particulate air pollution and annual mortality rates, but these studies have been criticized, in part because they did not directly control for cigarette smoking and other health risks. Methods In this prospective cohort study, we estimated the effects of air pollution on mortality, while controlling for individual risk factors. Survival analysis, including Cox proportional-hazards regression modeling, was conducted with data from a 14-to-16-year mortality follow-up of 8111 adults in six U.S. cities. Results Mortality rates were most strongly associated with cigarette smoking. After adjusting for smoking and other risk factors, we observed statistically significant and robust associations between air pollution and mortality. The adjusted mortality-rate ratio for the most po...
7,194 citations
TL;DR: Increased mortality is associated with sulfate and fine particulate air pollution at levels commonly found in U.S. cities, although the increase in risk is not attributable to tobacco smoking, although other unmeasured correlates of pollution cannot be excluded with certainty.
Abstract: Time-series, cross-sectional, and prospective cohort studies have observed associations between mortality and particulate air pollution but have been limited by ecologic design or small number of subjects or study areas. The present study evaluates effects of particulate air pollution on mortality using data from a large cohort drawn from many study areas. We linked ambient air pollution data from 151 U.S. metropolitan areas in 1980 with individual risk factor on 552,138 adults who resided in these areas when enrolled in a prospective study in 1982. Deaths were ascertained through December, 1989. Exposure to sulfate and fine particulate air pollution, which is primarily from fossil fuel combustion, was estimated from national data bases. The relationships of air pollution to all-cause, lung cancer, and cardiopulmonary mortality was examined using multivariate analysis which controlled for smoking, education, and other risk factors. Although small compared with cigarette smoking, an association between mor...
2,792 citations