Maintaining the long-term accuracy of water distribution models with data assimilation methods: A comparative study

Zhou, Xiao; Guo, Shuyi; Xin, Kunlun; Xu, Weirong; Tao, Tao; Yan, Hexiang

doi:10.1016/j.watres.2022.119268

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…3, the water source at single DWDN locations might fluctuate between different DWTPs due to the instantaneous water demands which might deviate from the ones collected for model calibration, 77 limiting the usefulness of traditional DWDS models in supporting day to day management. Digital Twins 87 and data assimilation techniques 88 could solve this limitation, employing immediately the DWDS sensor data for model refinement. In addition, the right attribution of a sampling location to a DWTP is fundamental in the case of an early-warning system focused on water quality, to put in place the most appropriate actions where they are actually needed.…”

Section: Implications For Dwds Monitoring and Managementmentioning

confidence: 99%

Source to tap investigation of natural organic matter in non-disinfected drinking water distribution systems

Gabrielli,

Pulcini,

Barbesti

et al. 2024

Environ. Sci.: Water Res. Technol.

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Section: Implications For Dwds Monitoring and Managementmentioning

confidence: 99%

Source to tap investigation of natural organic matter in non-disinfected drinking water distribution systems

Gabrielli,

Pulcini,

Barbesti

et al. 2024

Environ. Sci.: Water Res. Technol.

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“…Ruzza (2017) carried out a similar leak detection study in WDNs using KF, EnKF, Ensemble Smoothing, and Normal-Score EnKF to identify nodal leakages. Ensemble-based methods are also effective in providing stable calibration results to ensure the long-term accuracy of models as demonstrated by Zhou et al (2018Zhou et al ( , 2022.…”

Section: Introductionmentioning

confidence: 99%

A Multi-step Data Assimilation framework to investigate the effect of measurement uncertainty in the reduction of water distribution network model errors

Fayaz,

Alfonso,

Gama

2024

Preprint

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Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor's precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors' precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links.

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A Multi-step Data Assimilation Framework to Investigate the Effect of Measurement Uncertainty in the Reduction of Water Distribution Network Model Errors

Fayaz,

Castro-Gama,

Alfonso

2024

Water Resour Manage

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Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor’s precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors’ precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links.

show abstract

Maintaining the long-term accuracy of water distribution models with data assimilation methods: A comparative study

Cited by 5 publications

References 39 publications

Source to tap investigation of natural organic matter in non-disinfected drinking water distribution systems

Source to tap investigation of natural organic matter in non-disinfected drinking water distribution systems

A Multi-step Data Assimilation framework to investigate the effect of measurement uncertainty in the reduction of water distribution network model errors

A Multi-step Data Assimilation Framework to Investigate the Effect of Measurement Uncertainty in the Reduction of Water Distribution Network Model Errors

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