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• W2023835811 abstract "This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> S) in a gravity sewer system. The gaseous H <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths of the sewer network. A mutual information based strategy is used to choose the best k sensor measurements and their locations from among n potential sensor observations and their locations. This provably NP-hard combinatorial sensor selection problem is addressed by maximizing the mutual information between the selected locations and the locations that are not selected or do not have any sensor deployments. A proof-of-concept study was carried out comparing the spatial prediction of H <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> S with a complex model currently used by Sydney Water. The proposed approach is shown to be effective in both modelling and predicting the H <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> S spatial concentrations in sewers as well as identifying optimal number of H <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> S sensors and their locations for a required level of prediction accuracy." @default.
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• W2023835811 date "2014-12-01" @default.
• W2023835811 modified "2023-10-16" @default.
• W2023835811 title "Spatial prediction of hydrogen sulfide in sewers with a modified Gaussian process combined mutual information" @default.
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• W2023835811 doi "https://doi.org/10.1109/icarcv.2014.7064464" @default.
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