Automated Plume Rise Measurement based on Deep Neural Networks using Video Images

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dc.contributor.advisorGordon, Mark
dc.contributor.advisorSohn, Gunho
dc.contributor.authorKoushafar, Mohammad
dc.date.accessioned2024-03-18T18:02:58Z
dc.date.available2024-03-18T18:02:58Z
dc.date.issued2024-03-16
dc.date.updated2024-03-16T10:42:11Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractWhen a smokestack releases pollutants, the resulting plume cloud dissipates gradually and mixes with the surrounding air; it becomes neutrally buoyant and loses its vertical momentum. It is then carried downwind at a constant height called the Plume Rise or plume rise height. Plume rise affects how far pollutants are carried downwind, their deposition to the environment, and the amount of greenhouse gases mixed into the upper troposphere. Therefore, correctly calculating plume rise for the modelled dispersion of pollutants is of concern in air-quality transport models and local environment assessment cases. Recent studies have shown that the Briggs equations, which are a popular form of parameterization in models, significantly underpredict the plume rise. Modern computer vision methods allow the possibility of measuring plume rise under varied atmospheric conditions using video images. Most existing computer vision methods detect smoke clouds using an estimated bounding box without performing a segmentation down to the pixel level. Our proposed method can accurately detect and segment a plume cloud exiting from a smokestack and consider a hypothetical plume centerline based on an improved Deep Convolutional Neural Network (DCNN). We propose a Mask R-CNN model that can be applied for extracting the region of the plume cloud area of interest. Then, the proposed network is modified with our training dataset and used for detecting the hypothetical centerlines of the plume cloud. Finally, a comparative analysis was performed using meteorological data and smokestack measurements. This analysis involved comparing the plume rise and plume rise distance values estimated by the proposed framework with the obtained values from the Briggs parameterization equations. The outcomes confirmed a considerable underestimation of plume rise by the Briggs plume rise parameterization in the region. Using our model, the conventional plume rise model validation methods can be developed, and scientific grounds are provided for developing new physical models.
dc.identifier.urihttps://hdl.handle.net/10315/41904
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subjectRemote sensing
dc.subjectAtmospheric sciences
dc.subject.keywordsPlume rise
dc.subject.keywordsDeep learning
dc.subject.keywordsPlume cloud recognition
dc.titleAutomated Plume Rise Measurement based on Deep Neural Networks using Video Images
dc.typeElectronic Thesis or Dissertation

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