Data-driven water quality prediction in chloraminated systems

2020 ◽  
Vol 5 (4) ◽  
pp. 1-19
Author(s):  
Andrew Peters ◽  
Bin Liang ◽  
Hongda Tian ◽  
Zhidong Li ◽  
Corinna Doolan ◽  
...  
Keyword(s):  
Water Quality ◽  
Decision Making ◽  
Delivery System ◽  
Hydraulic System ◽  
Data Driven ◽  
Quality Prediction ◽  
Key Factors ◽  
Water Quality Prediction ◽  
Parametric Decay ◽  
Upstream Reservoir

This paper proposes a data-driven method that provides water quality prediction within the entire Woronora delivery system in Sydney. Specifically, the key factors relating to water quality are identified through factor analysis. A Bayesian parametric decay model is formulated using the key factors to predict water quality. To estimate the water travel time, which links the upstream (reservoir) data to the downstream (resident) data, the hydraulic system is employed to capture the topology of the delivery system. Moreover, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making.

Integrated modelling under uncertainty in watershed-level assessment and management

2007 ◽  
Vol 56 (8) ◽  
pp. 31-39 ◽  
Author(s):  
J.H. Ham ◽  
C.G. Yoon ◽  
K.W. Jung ◽  
J.H. Jang
Keyword(s):  
Water Quality ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Watershed Management ◽  
Management Practices ◽  
Control Measures ◽  
Integrated Modelling ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Modelling System

Uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modelling system (modified-BASINS) under uncertainty is described and demonstrated for use in receiving-water quality prediction and watershed management. A Monte Carlo simulation was used to investigate the effect of various uncertainty types on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorus (T-P) in the Hwaong Reservoir, considering three uncertainty types, would be less than about 4.4 and 0.23 mg L−1, respectively, in 2012, with 90% confidence. The effects of two watershed management practices, wastewater treatment plants (WWTP) and constructed wetlands (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaong Reservoir to less than 3.4 and 0.14 mg L−1, 24 and 41% improvements, respectively, with 90% confidence. Overall, the Monte Carlo simulation in the integrated modelling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on the probability and level of risk, and its application is recommended.

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