An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai
type: Dataset
description: Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant environmental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud based on relationship of PM2.5 with relative humidity. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model. Source code and dataset We implement our proposed integrated algorithm with Python 3 and C++ programming language. We process the satellite images with OpenCV library. Keras library functions are used to implement our customized VGG Net. We write python script smallervggnet.py to build this VGG Net. Next, we train and test this network…
modified: 2024-06-19T11:56:01.198413Z
issued: 2024-06-19T11:56:01.198413Z
publisher: Luleå University of Technology
access rights: PUBLIC
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Sami Kabir
    type: Agent
    account: https://orcid.org/0000-0001-5283-6641
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Raihan Ul Islam
    type: Agent
    account: https://orcid.org/0000-0002-3090-7645
    type: PrivateIndividual(s)
qualified attribution: Attribution
  had role: principalInvestigator
  agent: Karl Andersson
    type: Agent
    type: PrivateIndividual(s)
keyword: Air Quality
keyword: Pervasive Mobile Computing
keyword: Expert System
keyword: Artificial Intelligence
keyword: Uncertainty
keyword: Nonlinear Model Predictive Control
keyword: Convolutional Neural Network
language: ENG

Landing page for the dataset
type: Distribution
title: Landningssida för datasetet @sv
access URL: https://doi.org/
availability: stable

Luleå University of Technology
type: Agent
name: Luleå tekniska universitet @sv
type: Academia-ScientificOrganisation
source: 016st3p78