Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2023
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| 在线阅读: | http://hdl.handle.net/10361/22002 |
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10361-220022023-12-20T04:49:00Z Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network Mondal, Joyanta Jyoti Rhidi, Nowsin Kabir Mukta, Jannatun Noor Manab, Meem Arafat Department of Computer Science and Engineering, Brac University Air quality index Picture-based Predictor of PM2.5 Concentration (PPPC) Deep learning Machine learning. Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 66-69). Although smartphones have already become the de facto tool for environmental health research for their ubiquity and portability, utilizing them in finding location specific aggregated air quality index based on PM2.5 concentration is little ex plored in the literature to date. In this paper, therefore, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos cap tured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos cap tured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model’s ability to act as a Picture-based Predictor of PM2.5 Concen tration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone. Here, the computation necessary in our model is comparatively resource-efficient, as our model subsumes a much smaller number of parameters compared to most of the other alternatives. Moreover, our experimental results show that our model exhibits more robustness, for location specific PM2.5 prediction than existing state-of-the-art models such as ViT (Vision Transformer) and INN (Involutional Neural Network) as well as other popular mod els that are created based on CNN, such as VGG19, ResNet50, or MobileNetV2. Joyanta Jyoti Mondal Nowsin Kabir Rhidi B.Sc. in Computer Science and Engineering 2023-12-18T06:24:12Z 2023-12-18T06:24:12Z 2023 2023-01 Thesis ID: 19141016 ID: 19101488 http://hdl.handle.net/10361/22002 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 69 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Air quality index Picture-based Predictor of PM2.5 Concentration (PPPC) Deep learning Machine learning. Neural networks (Computer science) |
| spellingShingle |
Air quality index Picture-based Predictor of PM2.5 Concentration (PPPC) Deep learning Machine learning. Neural networks (Computer science) Mondal, Joyanta Jyoti Rhidi, Nowsin Kabir Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Mukta, Jannatun Noor |
| author_facet |
Mukta, Jannatun Noor Mondal, Joyanta Jyoti Rhidi, Nowsin Kabir |
| format |
Thesis |
| author |
Mondal, Joyanta Jyoti Rhidi, Nowsin Kabir |
| author_sort |
Mondal, Joyanta Jyoti |
| title |
Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| title_short |
Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| title_full |
Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| title_fullStr |
Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| title_full_unstemmed |
Finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| title_sort |
finding location-specific aggregated air quality index with smartphone images using deep convolutional neural network |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/22002 |
| work_keys_str_mv |
AT mondaljoyantajyoti findinglocationspecificaggregatedairqualityindexwithsmartphoneimagesusingdeepconvolutionalneuralnetwork AT rhidinowsinkabir findinglocationspecificaggregatedairqualityindexwithsmartphoneimagesusingdeepconvolutionalneuralnetwork |
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