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Predictive Modelling of Gas Concentrations in Tunnels Using Machine Learning.
Date
2023Type
ThesisDepartment
Mining Engineering
Degree Level
Master's Degree
Abstract
This study introduces a machine learning methodology for predicting gas concentrationsat specific location within a tunnel model. The machine learning model is trained using
gas concentration data obtained from sensors placed at diverse locations. The procedural
sequence commences with the acquisition of data through an experimental protocol
designed for training the machine learning model. Subsequently, the K-Nearest Neighbor
(KNN) model is employed for predictive computations. The efficacy of the model is
assessed through a comprehensive case study. The findings demonstrate that the proposed
methodology exhibits a high level of accuracy, affirming its robust performance in
predicting gas concentrations within the tunnel model.
Permanent link
http://hdl.handle.net/11714/10873Additional Information
Committee Member | Danko, George; Feng, Jia |
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