Flowrate Time Series Processing in Engineering Tools for Water Distribution Networks

Abstract

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.

GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm

“…In water distribution networks, a time series is a collection of data points over time, and can provide insights into the behavior and performance of a system. This type of data typically includes variables such as flow rates, pressure levels, water quality parameters, and other relevant metrics recorded at regular intervals [16] . Numerous studies have explored the use of time series data for leak detection in water distribution systems, with examples including modified fuzzy evolving algorithms [ 14 , 17 ]and methods based on parameter determination and performance evaluations [ 14 , 18 ].…”

Computational Tools for Supporting the Operation and Management of Water Distribution Systems towards Digital Transformation

“…The implemented data processing tool is based on the methodology proposed by Ferreira et al [6] that allows the automatic processing of flow rate time series 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). The methodology consists of a four-step procedure: (i) anomaly identification and removal, (ii) short-duration gap reconstruction, (iii) time step normalization, and (iv) long-duration gap reconstruction.…”

“…These parameters include the time step after normalization, and parameters related to thresholds for anomalous values' identification (see parameter box on the right side of Figure 3). More details regarding the parameters of the processing methodology can be found in Ferreira et al's work [6]. Already processed flow rate time series of the same sensor can also be imported to be used in the reconstruction step.…”

“…Water services bring together a wide variety of collected data (e.g., flow rate, pressure, energy consumption, end-user consumption) [3][4][5]. Depending on the water utility maturity level, different data can be collected with different acquisition frequencies, manually in situ by an operator (e.g., domestic consumption) or automatically by smart meters or by sensors installed in the systems (e.g., flow rate, pressure) [6]. Independently of the type of metering used, data are periodically collected and should provide good knowledge about water distribution systems (WDS).…”

Application of Robot Automation Technology Based on Machine Assisted and Artificial Intelligence in Distribution Network Overhead Line Engineering

“…Before artificial intelligence was created, humans relied on fixed instructions to make robots perform mechanical repetitive work, which made robots only perform some simple and repetitive assembly line work, usually used in industrial production activities. And after the creation of AI, robots seem to be endowed with human abilities to think and learn like humans and perform some high-end and complex tasks and tasks [5].…”