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An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-931 87 Skellefteå, Sweden
Department of Computer Science & Engineering, University of Chittagong, Chattogram 4331, Bangladesh
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1956;
Received: 5 March 2020 / Revised: 25 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Section Physical Sensors)
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.
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Keywords: BRBES; Deep Learning; integration; sensor data; predict BRBES; Deep Learning; integration; sensor data; predict
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MDPI and ACS Style

Kabir, S.; Islam, R.U.; Hossain, M.S.; Andersson, K. An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution. Sensors 2020, 20, 1956.

AMA Style

Kabir S, Islam RU, Hossain MS, Andersson K. An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution. Sensors. 2020; 20(7):1956.

Chicago/Turabian Style

Kabir, Sami; Islam, Raihan U.; Hossain, Mohammad S.; Andersson, Karl. 2020. "An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution" Sensors 20, no. 7: 1956.

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