Application of machine learning methods for nutrient prediction in urban catchments

Benya Wang

Research output: Thesis › Doctoral Thesis

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Abstract

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.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Award date28 May 2019
DOIs
Publication statusUnpublished - 2019

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catchment
water quality
nutrient
prediction
interpolation
coastal water
urbanization
estuary
surface water
oxygen
groundwater
machine learning
method
modeling
simulation
water
thesis

Cite this

Wang, Benya. / Application of machine learning methods for nutrient prediction in urban catchments. 2019.
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title = "Application of machine learning methods for nutrient prediction in urban catchments",
abstract = "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.",
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doi = "10.26182/5d159a40cfde1",
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school = "The University of Western Australia",

}

Wang, B 2019, 'Application of machine learning methods for nutrient prediction in urban catchments', Doctor of Philosophy, The University of Western Australia. https://doi.org/10.26182/5d159a40cfde1

Application of machine learning methods for nutrient prediction in urban catchments. / Wang, Benya.

2019.

Research output: Thesis › Doctoral Thesis

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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

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Wang B. Application of machine learning methods for nutrient prediction in urban catchments. 2019. https://doi.org/10.26182/5d159a40cfde1

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  • THESIS - DOCTOR OF PHILOSOPHY - WANG Benya - 2019

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