An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The Swan-Canning Estuary virtual observatory

Huang, Peisheng; Trayler, K.M.; Wang, Benya; Saeed, Amina; Oldham, Carolyn; Busch, Brendan

doi:10.1016/j.jmarsys.2019.103218

Cited by 30 publications

(12 citation statements)

References 76 publications

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“…However, if the improved model performance is higher than the introduced error, the results are manageable. Similar results were also found in Hunter et al (2018), who compared a hybrid process-driven and ANN model with the stand-alone ANN model and the process-driven model. In their study, the hybrid also achieved the best performance followed by standalone ANN.…”

Section: A Comprehensive Comparison Of Six Modelssupporting

confidence: 81%

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients

2020

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Abstract. Nutrient data from catchments discharging to receiving waters are monitored for catchment management. However, nutrient data are often sparse in time and space and have non-linear responses to environmental factors, making it difficult to systematically analyse long- and short-term trends and undertake nutrient budgets. To address these challenges, we developed a hybrid machine learning (ML) framework that first separated baseflow and quickflow from total flow, generated data for missing nutrient species, and then utilised the pre-generated nutrient data as additional variables in a final simulation of tributary water quality. Hybrid random forest (RF) and gradient boosting machine (GBM) models were employed and their performance compared with a linear model, a multivariate weighted regression model, and stand-alone RF and GBM models that did not pre-generate nutrient data. The six models were used to predict six different nutrients discharged from two study sites in Western Australia: Ellen Brook (small and ephemeral) and the Murray River (large and perennial). Our results showed that the hybrid RF and GBM models had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species across the two sites. The pre-generated nutrient and hydrological data were highlighted as the most important components of the hybrid model. The model results also indicated different hydrological transport pathways for total nitrogen (TN) export from two tributary catchments. We demonstrated that the hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of responses of surface water nutrient concentrations to hydrologic variability.

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Section: A Comprehensive Comparison Of Six Modelssupporting

confidence: 81%

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients

2020

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“…Daily salinity data extracted from the Swan‐Canning Estuary Response Model (SCERM v2; Huang et al., 2019) for each site were strongly correlated with in situ data collected by DWER, providing further confidence in the model and associated outputs (Figure ). Salinity data generated by the model were used for the following periods: December–May (2014/2015 and 2015/2016; ‘background’) and February to March 2017 (‘event’).…”

Section: Methodsmentioning

confidence: 78%

“…The Swan‐Canning is a shallow micro‐tidal (<1 m) estuary that covers approximately 40 km 2 and flows through Perth, the capital city of Western Australia (Figure 1). Like estuaries world‐wide, the Swan–Canning shows increasing signs of eutrophication including more frequent fish kills and algal blooms (Huang et al., 2019). Around 50% of the Perth population occupies the coastal catchments, so the lower and middle estuarine reaches receive nutrients from urban sources while nutrient inputs to the upper estuary and river zones originate from agriculture and light industrial activities (Kelsey et al., 2010).…”

Section: Methodsmentioning

confidence: 99%

Population‐specific resilience of Halophila ovalis seagrass habitat to unseasonal rainfall, an extreme climate event in estuaries

Webster

Kilminster

Alarcón³

et al. 2021

Journal of Ecology

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1. Extreme climate events are predicted to alter estuarine salinity gradients exposing habitat-forming species to more frequent salinity variations. The intensity and duramost estuarine biota will be resistant to some fluctuations in salinity via phenotypic plasticity in salinity tolerance, so the ecosystem resilience will depend upon the ability to resist and/or recover from the disturbance. This in turn is dependent upon the characteristics of the disturbance such as the direction, intensity, magnitude of change and the duration of exposure (Kültz, 2015;Lee & Petersen, 2003). Scientists are using frameworks to define ECEs such as marine heatwaves (Hobday et al., 2016), and to understand accurate predictions about ecological responses to climate change and identifies which populations may 'future proof' ecosystem resilience. 6. Synthesis. Following an extreme rainfall event, we found seagrass populations that are exposed to variable salinities recovered while those from a stable salinity environment were unable to recover within the study time frame. These findings expand upon existing evidence, derived primarily from other ecosystems, that show new sources of resilience may be uncovered by accounting for between-population variation.

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“…The pre-generation of missing data and pre-modelling hydrological analysis were critical components of the hybrid model and allowed identification of the impact of different hydrological transport pathways for TN export from the two tributary catchments. The hybrid ML methods were further applied to generate nutrient data for eight tributaries, and the generated data have since been used as inputs to an estuary prediction model, which simulates and forecasts nutrient concentrations in the previous and next five days in the Swan-Canning Estuary (Huang et al, 2019). The modelling methods 460 and strategies developed in the work presented here, can be easily applied to other study areas.…”

Section: The Application Of ML Methods For Hydrological Modellingmentioning

confidence: 99%

ML-SWAN-v1: a hybrid machine learning framework for the prediction of daily surface water nutrient concentrations

Wang

Hipsey

Oldham

2020

Preprint

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Abstract. Nutrient data from catchments discharging to receiving waters are necessary to monitor and manage water quality, however, they are often sparse in time and space and have non-linear responses to environmental factors, making it difficult to systematically analyse long- and short-term trends and undertake nutrient budgets. To address these challenges, we developed a hybrid machine learning (ML) framework that first separated baseflow and quickflow from total flow, and then generated data for missing nutrient species, using relationships with hydrological data, rainfall, and temporal data. The generated nutrient data were then included as additional variables in a final simulation of tributary water quality. Hybrid random forest (RF) and gradient boosting machines (GBM) models were employed and their performance compared with a linear model, a multivariate weighted regression model and stand-alone RF and GBM models that did not pre-generate nutrient data. The six models were used to predict TN, TP, NH3, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and filterable reactive phosphorus (FRP) discharged from two study sites in Western Australia: Ellen Brook (small and ephemeral) and the Murray River (large and perennial). Our results showed that the hybrid RF and GBM models had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species across the two sites. We demonstrated that the hybrid model provides a flexible method to combine data of varied resolution and quality, and is accurate for the prediction of responses of surface water nutrient concentrations to hydrologic variability.

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An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The Swan-Canning Estuary virtual observatory

Cited by 30 publications

References 76 publications

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients

Population‐specific resilience of Halophila ovalis seagrass habitat to unseasonal rainfall, an extreme climate event in estuaries

ML-SWAN-v1: a hybrid machine learning framework for the prediction of daily surface water nutrient concentrations

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