Dataset Information

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting.

ABSTRACT: Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, we determined PM[Formula: see text] as the target pollutant and designed a new deep learning based air quality forecasting model, namely DFS (Deep Flexible Sequential). Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before. DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). The proposed model also is capable of generalization with standard and flexible Dropout layers. Through flexible Dropout layer, the model also obtains flexibility to adapt changing window sizes in sequential modelling. Moreover, this model can be applied to other air pollution time series data problems with small modifications on parameters by taking into account the nature of the data set.

SUBMITTER: Kaya K

PROVIDER: S-EPMC7042334 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting.

Kaya Kıymet K Gündüz Öğüdücü Şule Ş

Scientific reports 20200225 1

Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, ...[more]

PMID: 32098977

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Project description:Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

Dataset Information

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting.

Publications

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting.

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