Comparison of Reconstruction Methods for Water Supply Systems Flow Rate Time Series

Ascensão, Carlos; Ferreira, Bruno; Barreira, Raquel; Carriço, Nelson

doi:10.1007/978-3-030-75315-3_90

Cited by 1 publication

(3 citation statements)

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“…Nonetheless, problems may arise when attempting to use the ARIMA model to predict the total volume of a holiday occurring during a weekday (Ascensão et al., 2021). As such, improvements to the original method are carried in the daily scale model to account for this possibility.…”

Section: Methodology For Flowrate Time Series Processingmentioning

confidence: 99%

“…These measurement errors, typically known as outliers or anomalous values (Kirstein et al., 2019), should be detected and corrected before they can be used in engineering applications (e.g., hydraulic modeling, calibration, leak detection) (Romano et al., 2014). Gaps of non‐validated measurements must be reconstructed with estimated data, being different methods used to this purpose (Ascensão et al., 2021). Note that flowrate time series usually present daily and weekly patterns which must be considered by the used reconstruction techniques (Barrela et al., 2017; Kirstein et al., 2019; Quevedo et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The fourth and last step is the reconstruction of long duration gaps using the technique presented in Quevedo et al (2010). This technique accounts for the daily and weekly cycles of flowrate time series, being able to correctly reconstruct gaps during and weekends and failing when reconstructing holidays that occur in weekdays (Ascensão et al, 2021). This paper presents improvements to be carried to the original technique when reconstructing holidays using a pattern model coupled with simple exponential smoothing and by using a subset of past holidays and Sundays.…”

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confidence: 99%

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Flowrate Time Series Processing in Engineering Tools for Water Distribution Networks

Ferreira

Carriço

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et al. 2022

Water Resources Research

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The current paper presents a comprehensive methodology for processing unevenly (and evenly) spaced flowrate time series for subsequent use in engineering tools, such as the calibration of hydraulic models or the detection and location of leaks and bursts. The methodology is a four‐step procedure: (a) anomaly identification and removal, (b) short‐duration gap reconstruction, (c) time step normalization, and (d) long‐duration gap reconstruction. The time step normalization is carried out by a numerical procedure prior to the reconstruction process. This reconstruction process uses a pattern model coupled with regression techniques (i.e., autoregressive integrated moving average and exponential smoothing). The methodology is calibrated using Monte Carlo simulations applied to a water utility flowrate time series and validated with two additional time series from different water utilities. Obtained results demonstrate that the proposed methodology can process flowrate time series from water supply systems with different characteristics (e.g., consumption pattern, data acquisition system, transmission settings) both for normal operating conditions and during the occurrence of abnormal events (e.g., pipe bursts). This methodology is a very useful tool for the daily management of water utilities, preparing the time series to be used in different engineering tools, namely, hydraulic simulation, model calibration or online burst detection.

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Section: Methodology For Flowrate Time Series Processingmentioning

confidence: 99%