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An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-5283-6641
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
Department of Computer Science & Engineering, University of Chittagong, Chattogram, Bangladesh.ORCID iD: 0000-0002-7473-8185
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 7, p. 1-25, article id 1956Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2020. Vol. 20, no 7, p. 1-25, article id 1956
Keywords [en]
BRBES, Deep Learning, integration, sensor data, predict
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-78258DOI: 10.3390/s20071956ISI: 000537110500152PubMedID: 32244380Scopus ID: 2-s2.0-85083042302OAI: oai:DiVA.org:ltu-78258DiVA, id: diva2:1420845
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networksPERvasive Computing and COMmunications for sustainable development
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2020;Nivå 2;2020-04-01 (cisjan)

Available from: 2020-03-31 Created: 2020-03-31 Last updated: 2024-04-24Bibliographically approved
In thesis
1. An Improved Belief Rule-Based Expert System with an Enhanced Learning Mechanism
Open this publication in new window or tab >>An Improved Belief Rule-Based Expert System with an Enhanced Learning Mechanism
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Ett förbättrat BRB-baserat expertsystem med en utvecklad inlärningsmekanism
Abstract [en]

Belief rule-based expert systems (BRBESs) are widely used in various domains which provide an integrated framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. The correctness of the data significantly affects the accuracy of the BRBESs. Learning plays an important role in BRBESs to upgrade their knowledge base and parameters values, necessary to improve the accuracy of prediction. In addition, comparatively larger datasets hinder the accuracy of BRBESs.

Therefore, this doctoral thesis focuses on four different aspects of BRBESs, namely, the accuracy of data, multi-level complex problem, learning of BRBES, and accuracy of prediction for comparatively large dataset.

First, the accuracy of data acquisition plays an important role, necessary to ensure accurate prediction in BRBESs. Therefore, the data coming from sensors contain anomaly due to various types of uncertainty, which hampers the accuracy of prediction. Hence, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis.

Second, BRBESs can be considered to handle the multi-level complex problem like the prediction of a flood as they address different types of uncertainty. A web based BRBES was developed for predicting flood which provides better usability, allows handling of larger numbers of rule bases, and facilitates scalability. In addition, a learning mechanism for multi-level BRBESs has been developed by taking account of flooding, considered as an example of a complex problem. This learning mechanism for multi-level BRBES demonstrates promising results in comparison to other machine learning techniques including, Long Short-term Memory (LSTM), Artificial neural network (ANN), Support Vector Machine (SVM), and Linear regression.

Third, different optimal training procedures used to support learning in BRBESs. Among these, Differential Evolution (DE) appears performing better in comparison to other evolution algorithms, including Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). However, DE's performance depends considerably in assigning near-optimal values to its control parameters. Therefore, an enhanced belief rule-based adaptive differential evolution (eBRBaDE) proposed in this thesis with the capability of ensuring balanced exploitation and exploration in the search space by providing near-optimal values to the DE's control parameters. The capability of accurate prediction of eBRBaDE has been demonstrated by taking account of power usage effectiveness (PUE) of datacentre in comparison to other evolutionary algorithms used in BRBESs optimal training procedures.

Fourth, the recent advancement of sensor technologies enabled acquiring of a huge amount of data. In this context, deep learning appears as an effective method to process this huge amount of data. However, this high volume of data contains various types of uncertainties, including vagueness, imprecision, randomness, ignorance and incompleteness. Hence, an enhanced deep learning approach, named BRB-DL, has been developed by integrating BRBES, allowing the improvement of prediction accuracy, especially in case of a large dataset. The applicability of this BRB-DL has been carried out by considering a large amount of air pollution data to predict the air quality index (AQI) of different Chinese cities.

In the light of the above, it can be argued that the novel anomaly detection algorithm proposed in this thesis enables the removing of anomalous data. The proposed learning mechanism for multi-level BRBES allows handling of the multi-level complex problem. The optimal training procedure, named eBRBaDE, enabling determination of optimal learning parameters of BRBESs and finally, the integration of deep learning with BRBES allows to handle large data set.

Place, publisher, year, edition, pages
Luleå University of Technology, 2020
Series
Doctoral thesis / Luleå University of Technologyy… → 31 dec 1996, ISSN 0348-8373
National Category
Computer and Information Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78762 (URN)978-91-7790-599-8 (ISBN)978-91-7790-600-1 (ISBN)
Public defence
2020-06-10, Hörsal A, Campus Skellefteå, Forskargatan 1, 931 62, Skellefteå, 10:00 (English)
Opponent
Supervisors
Available from: 2020-05-04 Created: 2020-05-04 Last updated: 2023-09-05Bibliographically approved
2. A Novel Explainable Belief Rule-Based Prediction Framework under Uncertainty
Open this publication in new window or tab >>A Novel Explainable Belief Rule-Based Prediction Framework under Uncertainty
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Traditional Machine Learning (ML) and Deep Learning (DL) models provide very accurate predictions because of their intricate mathematical operations. However, these models do not explain the reasons in support of the predictive outputs. Therefore, there is no trust between humans and AI when it produces such obtuse results. The widespread adoption of AI models heavily relies on the trust that humans place in the decisions made by AI. This trust holds great importance in safety-critical sectors like healthcare, autonomous vehicles, and the energy domain. Several post-hoc tools can help elucidate the results of AI models. The use of training datasets rather than domain knowledge renders such an explanation as a proxy. A misleading explanation will be produced by a biased training dataset. In contrast, an end user is more likely to believe an explanation that is grounded in domain knowledge. Motivated by this, we propose a novel framework, consisting of Belief Rule Based Expert System (BRBES), to predict output and explain it with reference to domain knowledge. BRBES effectively utilizes its rule base to represent domain knowledge and adeptly handles uncertainty resulting from a lack of information. We also fine-tune the parameters and structure of BRBES to enhance the accuracy of the prediction. Therefore, the output of our proposed framework is not only accurate, but also easily explainable.

This licentiate thesis delves into the challenges and opportunities surrounding eXplainable ArtificialIntelligence (XAI) in order to provide a comprehensive understanding of AI output. It presents a new XAI framework that can effectively predict an output and provide explanations based on domain knowledge. In addition, it shows how effective BRBES integration can be by integrating it with a deep learning model to forecast air quality phenomenon using ground and satellite data.

This thesis presents four significant contributions. First, we conduct a comprehensive examination ofthe existing literature on XAI, delving into the numerous challenges and opportunities it offers. Extensive research has been conducted to explore the definition, classification, and practical use of XAI. In addition, this complex study highlights the significance of the user interface in effectively communicating explanations in a way that is comprehensible to humans. It also brings up the tradeoff between explainability and accuracy in XAI. Secondly, we present an innovative explainable BRBES (eBRBES) model that offers accurate predictions of building energy consumption phenomenon while providing insightful explanations based on domain knowledge. As part of eBRBES, we also present a novel Belief Rule Based adaptive Balance Determination (BRBaBD) algorithm to assess the optimal balance between explainability and accuracy. Thirdly, we propose a mathematical model to integrate BRBES with the Convolutional Neural Network (CNN). We leverage the domain knowledge of BRBES, and hidden data patterns discovered by CNN with this integrated approach. We predict air quality withthis integrated model using outdoor ground images and sensor data. Fourth, we integrate two-layer BRBES with CNN to monitor air quality using satellite images, and relevant environmental parameters, such as cloud, relative humidity, temperature, and wind speed. The two-layer BRBES showcases the strength of BRBES in conducting reasoning across multiple layers.

Based on the research findings of this thesis applied on two different phenomena, it can be argued that utilizing a belief rule-based framework can enhance predictability with greater clarity and precision. 

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-105200 (URN)978-91-8048-554-8 (ISBN)978-91-8048-555-5 (ISBN)
Presentation
2024-06-20, A193, Luleå University of Technology, Skellefteå, 09:00 (English)
Opponent
Supervisors
Funder
Vinnova, 2022-01188
Note

Funder: Rönnbäret Foundation, Sweden

Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2024-05-30Bibliographically approved

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Kabir, SamiIslam, Raihan UlHossain, Mohammad ShahadatAndersson, Karl

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