Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data

Li, Zilin; Liu, Haixing; Zhang, Chi; Fu, Guangtao

doi:10.1016/j.watres.2023.121018

Cited by 23 publications

(1 citation statement)

References 42 publications

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“…Li et al (2024a) propose GNNs for identifying contamination sources in water distribution systems, and for water quality prediction (Z. Li et al, 2024b). Other recent works on GNNs for pressure estimation are (Ashraf et al, 2023;Hajgató et al, 2021aHajgató et al, , 2021b.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks for Pressure Estimation in Water Distribution Systems

Truong,

Tello,

Lazovik

et al. 2024

Water Resources Research

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Pressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics‐based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data‐driven approaches have gained traction to overcome such limitations. In this work, we combine physics‐based modeling and graph neural networks (GNN), a data‐driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN‐based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real‐world scenarios. As a result, a new state‐of‐the‐art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average.

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Section: Introductionmentioning

confidence: 99%

Graph Neural Networks for Pressure Estimation in Water Distribution Systems

Truong,

Tello,

Lazovik

et al. 2024

Water Resources Research

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A CFD‐PBM‐ANN framework to simulate the liquid–liquid two‐phase flow in a pulsed column

Wang,

Ma,

Zhou

et al. 2024

AIChE Journal

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CFD‐PBM numerical simulation is a powerful tool in the research of droplet swarm behavior. In this work, an artificial neural network (ANN) based droplet breakage frequency function is established based on the directly measured data from our previous studies. Then, the weights and biases of ANN are embedded into the CFD‐PBM code in the form of matrices and vectors. For the first time, a CFD‐PBM‐ANN simulation framework is established. Simulation results are in good agreement with the experimental data under different operation conditions. The cumulative droplet size distribution decreases with the increase of interfacial tension and pulse intensity. It is also found by the simulation that the droplet breakage frequency is relatively high at the edge of disc and doughnut plate, which is accordant with the distribution of turbulent energy dissipation and velocity gradient.

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Enhancing reservoir inflow forecasting precision through Bayesian Neural Network modeling and atmospheric teleconnection pattern analysis

Vasheghani Farahani,

Massah Bavani,

Roozbahani

2024

Stoch Environ Res Risk Assess

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Via the framework of this research, a Bayesian Neural Network (BNN) machine learning model integrated with atmospheric teleconnection patterns was employed to predict the monthly inflow to three major dams (Zayanderood, Amirkabir, and Karun 3) in Iran. The impact of eight teleconnection indices throughout 1 to 12 months, as well as local station variables such as precipitation and inflow, was assessed. Optimal input variables and time delays were determined utilizing the Mutual Information index, identifying specific teleconnection patterns as significant influencers on dam inflow. The performance of the BNN model was compared to an Artificial Neural Network (ANN) model using both deterministic and probabilistic metrics. For deterministic evaluation, the Normalized Root Mean Square Error (NRMSE) for the BNN model in the best prediction scenarios was 10.93%, 9.07%, and 7.55% for Zayanderood, Amirkabir, and Karun 3 dams, respectively. The corresponding values for the ANN model were 12.27%, 10.72%, and 7.71%. Additionally, probabilistic evaluation using CRPS demonstrated that BNN outperformed ANN in the test phase, with CRPS values of 8.98 m3/s compared to 14.69 m3/s (Zayanderood), 1.77 m3/s compared to 3.17 m3/s (Amirkabir), and 36.16 m3/s compared to 51.22 m3/s (Karun 3), highlighting BNN’s superior predictive skill. Despite these results, both models exhibited limitations in accurately predicting inflow peaks. This study highlights the potential of teleconnection patterns as predictive variables for dam inflow and underscores the importance of further exploration across different regions. Using BNN for dam inflow prediction is a significant contribution to the field of hydrological forecasting and offers a generalizable approach for incorporating large-scale climate patterns into water resources management.

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Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data

Cited by 23 publications

References 42 publications

Graph Neural Networks for Pressure Estimation in Water Distribution Systems

Graph Neural Networks for Pressure Estimation in Water Distribution Systems

A CFD‐PBM‐ANN framework to simulate the liquid–liquid two‐phase flow in a pulsed column

Enhancing reservoir inflow forecasting precision through Bayesian Neural Network modeling and atmospheric teleconnection pattern analysis

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