Abstract
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Award date | 28 May 2019 |
DOIs | |
Publication status | Unpublished - 2019 |
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Application of machine learning methods for nutrient prediction in urban catchments. / Wang, Benya.
2019.Research output: Thesis › Doctoral Thesis
TY - THES
T1 - Application of machine learning methods for nutrient prediction in urban catchments
AU - Wang, Benya
PY - 2019
Y1 - 2019
N2 - The impacts of urbanisation on water quality and long- and short-term water quality changes are of central concern in many estuaries and coastal waters. This thesis demonstrated that machine learning (ML) methods can be successfully used for interpolation and simulation of nutrient and oxygen concentrations, across a range of hydrological systems and hydrological conditions. ML models are an essential tool to fully utilise all available water quality and hydrological data for water quality modelling, to facilitate the exploration of spatial and temporal signals in groundwater and surface water nutrient data, and ultimately the management of receiving waters.
AB - The impacts of urbanisation on water quality and long- and short-term water quality changes are of central concern in many estuaries and coastal waters. This thesis demonstrated that machine learning (ML) methods can be successfully used for interpolation and simulation of nutrient and oxygen concentrations, across a range of hydrological systems and hydrological conditions. ML models are an essential tool to fully utilise all available water quality and hydrological data for water quality modelling, to facilitate the exploration of spatial and temporal signals in groundwater and surface water nutrient data, and ultimately the management of receiving waters.
KW - Water quality
KW - groundwater mapping
KW - hybrid machine learning
U2 - 10.26182/5d159a40cfde1
DO - 10.26182/5d159a40cfde1
M3 - Doctoral Thesis
ER -