Large-scale Water Distribution Networks Research Articles

Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8.

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis.

Study on Large-Scale Urban Water Distribution Network Computation Method Based on a GPU Framework

Large-scale urban water distribution network simulation plays a critical role in the construction, monitoring, and maintenance of urban water distribution systems. However, during the simulation process, matrix inversion calculations generate a large amount of computational data and consume significant amounts of time, posing challenges for practical applications. To address this issue, this paper proposes a parallel gradient calculation algorithm based on GPU hardware and the CUDA Toolkit library and compares it with the EPANET model and a model based on CPU hardware and the Armadillo library. The results show that the GPU-based model not only achieves a precision level very close to the EPANET model, reaching 99% accuracy, but also significantly outperforms the CPU-based model. Furthermore, during the simulation, the GPU architecture is able to efficiently handle large-scale data and achieve faster convergence, significantly reducing the overall simulation time. Particularly in handling larger-scale water distribution networks, the GPU architecture can improve computational efficiency by up to 13 times. Further analysis reveals that different GPU models exhibit significant differences in computational efficiency, with memory capacity being a key factor affecting performance. GPU devices with larger memory capacity demonstrate higher computational efficiency when processing large-scale water distribution networks. This study demonstrates the advantages of GPU acceleration technology in the simulation of large-scale urban water distribution networks and provides important theoretical and technical support for practical applications in this field. By carefully selecting and configuring GPU devices, the computational efficiency of large-scale water distribution networks can be significantly improved, providing more efficient solutions for future urban water resource management and planning.

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

Multi-level dynamic zoning design to improve the restorative capability of resilience: An emergence response in water contamination flushing

ABSTRACT The conventional district metered areas (DMAs) bounded by closed valves reduce space of emergency response actions to cope with system failure or emergency events for resilience improvement. In order to improve the restorative capability of a zoning water distribution system (WDS), a novel dynamic DMA optimization design method and a coupled emergency response strategy are proposed. A multi-level zoning method is applied to determine optimization of water source areas (WSAs), pressure management areas (PMAs), and dynamic DMAs. A coupled emergency response strategy of WDS zoning, valve closure, hydrant flushing, and dynamic DMA adjustment is proposed and validated in a real large-scale water distribution network. The results show that the coupled emergency response strategy based on dynamic DMA can enhance capacity for dynamic emergency response and improve resilience during contamination flushing.

A stepwise fast leakage localization method applying the strategy of dynamic area narrowing down for large-scale water distribution network

ABSTRACT Urban water distribution networks (WDNs) are facing serious leakage problems. As cities expand, the leakage localization burden on large WDNs gradually increases. Although methods have been widely researched, there is a lack of studies and successful applications for large-scale WDNs. To deal with this, a stepwise fast leakage localization Method, SFLLM, utilizing the `dynamic area narrowing down (DAND)' strategy and coupled leakage features (CLF) is proposed. The SFLLM includes a fast-and-dynamic stage using the DAND strategy to reduce the potential leakage area and an accurate localization stage. Only partial representative candidate locations are required to simulate leakages by DAND, and meantime CLF is used to analyze the leakage similarities so that the localization accuracy and efficiency can be improved. SFLLM was tested on a benchmark WDN, saving more than 88% of simulation by DAND strategy and achieving localization in 11 seconds. The results also proved the enhanced performance of CLF in ensuring the stability of the accuracy against various types of uncertainties that may occur in real WDNs. Moreover, three real burst leaks in an actual large-scale WDN were localized within 205 m in about 22 minutes by SFLLM, showing the method's reliable applicability in guiding field leak exploration.

Circulatory system-based optimization algorithm with dynamic penalty function for optimum design of large-scale water distribution networks

Water distribution networks (WDNs) are a crucial component of urban infrastructure, and their optimization plays a vital role in minimizing construction costs. However, existing heuristic algorithms for WDN optimization often rely on specific parameters, limiting their performance and hindering their applicability to diverse network configurations, particularly in large-scale systems. To address this challenge, a novel method called circulatory system-based optimization (CSBO) is proposed, which minimizes the need for algorithm-specific parameters and achieves optimal designs across WDNs of various scale. The impact of a dynamic penalty function tailored to pressure constraints on the optimal design is also investigated. This study encompasses the optimization of the Goyang and combined gravity networks and the large-scale Balerma irrigation and Kang and Lansey networks under identical conditions. The results of the standard and dynamic CSBO approaches prove that the proposed algorithms have a superior performance, particularly in convergence rate and stability, compared to previous methods.

Using complex network theory for missing data reconstruction in water distribution networks

Dealing with missing data is a critical challenge in analyzing water distribution networks (WDNs). These unknown data are often associated with the physical characteristics of pipes, like diameter, which are the foundations of several modeling endeavors. This study develops a fully automated model to reconstruct missing diameter information in WDNs within the complex network theory (CNT) framework. The newly developed model employs customized graph measures to systematically retrieve missing diameters based on the topological (e.g., connectivity) and hydraulic (e.g., flow) features of edges with available information. The proposed model is validated by testing it on three real-world WDNs located in Iran and Austria, where data gaps are created by randomly and progressively eliminating the pipe diameter information. Subsequently, the model's accuracy is assessed by comparing the diameter, pressure, and hydraulic-based resilience of the reconstructed WDNs with those of the complete dataset. The results indicate that the proposed reconstruction model can successfully retrieve missing information up to 90% of the data gap. In addition, the model is highly computationally efficient and can quickly reconstruct thousands of missing diameters. Our novel approach can benefit water utilities that frequently encounter incomplete data in their models and require missing data retrieval from large-scale WDNs.

Resilience enhancement of water distribution networks under pipe failures: a hydraulically inspired complex network approach

Abstract The resilience of water distribution networks (WDNs) should be proactively evaluated to reduce the potential impacts of disruptive events. This study proposes a novel hydraulically-inspired complex network approach (HCNA) to assess and enhance WDN resilience in the case of single-pipe failure. Unlike conventional hydraulic-based models, HCNA requires no hydraulic simulations for resilience analysis. Instead, it quantifies the failure consequences of edges (pipes) on the WDN graph by incorporating topological attributes with flow redistribution triggered by failures. This HCNA procedure leads to the identification of critical edges (pipes), as well as impacted ones, representing edges more susceptible to the failure of others. The impacted edges are then systematically resized by integrating HCNA with a graph-based design approach, obtaining a wide range of resilience enhancement solutions. A comparative study between HCNA and a hydraulic-based model for three WDNs confirms HCNA's effectiveness in identifying the most critical pipes in various network sizes. Furthermore, HCNA provides comparable resilience enhancement solutions with a hydraulic-based evolutionary optimization but with significantly lower computational effort (1,400 times faster). Thus, it can efficiently be used for resilience enhancement of large-scale WDNs, where the application of conventional optimizations is limited due to the intensive computational workload.

A fault-injection-based approach to leak localization in water distribution networks using an ensemble model of Bayesian classifiers

Leak localization in water distribution networks (WDNs) is essential to water management systems. Developing a reliable and robust leak localization technique is crucial for reducing water losses in large-scale WDNs. The challenge of leak localization in WDNs can be addressed using a model-based data-driven approach. Each node of a WDN is used as a category label by the classifier model for identifying leakages. As a result of the small number of sensors in a network and the uncertainties associated with the model parameters, the characteristics of some classes may be similar. Consequently, the algorithm's accuracy decreases. The method proposed in this paper uses the concept of fault injection and propagation in digital systems to separate the leak characteristics of the classes. This separation is done by creating a second leak with a known amount and location to propagate the effect of the first leak on the sensors. Various data sets are generated for each time sample by considering different values for the second leak. Finally, an ensemble of Bayesian classifiers simultaneously processes this data set to determine the leak's location. The accuracy of the proposed method has been evaluated using Hanoi and Modena network benchmarks. Experimental results show that the proposed method has 94.6% and 93.76 accuracy without using a time horizon technique, in the presence of all uncertainties for the Hanoi and Modena networks respectively which show more accurate results than existing methods‎.

Confident learning-based Gaussian mixture model for leakage detection in water distribution networks

Leakage detection in the water distribution system not only helps to reduce water waste but also decreases the risk of drinking water pollution. To reduce reliance on hardware devices and enable real-time detection, the water utilities are transitioning towards the data-driven based approach that relies on the analysis of the flow and pressure data collected from the supervisory control and data acquisition (SCADA) system. Due to the lack of leakage data, most of these methods are unsupervised methods that rely heavily on assumptions about the distribution of anomalies; whereas, the water utility's repair records contain much valid information about the leakage and normal characteristics. To convert this information into available labels and to address the lack of leakage data, this paper proposed a new leakage detection framework to infer the pressure characteristics under normal conditions based on historical data by combining a label cleaning method—confident learning (CL)—with an unsupervised method—Gaussian mixture model (GMM)—for leak detection. The methodology is validated with synthetic and real measured data. Comparisons with four unsupervised methods demonstrate that the GMM method has superior identification of leak features from pressure data. For a real-world water distribution system in K city containing 91 pressure sensors, the average true positive rate is 0.78 and the average false positive rate is 0.11. This methodology is a promising tool to identify signs of leakage in large-scale water distribution networks (WDNs).

Digital twin assisted decision support system for quality regulation and leak localization task in large-scale water distribution networks

Effective water resource management is essential in large metropolitan cities. Digital Twins (DT), supported by IIoT and machine learning technologies, provide opportunities for real-time prediction and optimization for effective decision-making in water distribution systems. A framework for the digital twin of the Water Distribution Network (WDN) is developed in this paper to achieve higher operational efficiency using ‘WNTR’, the Python-based library of EPANET. All computational experiments and methods were validated on the benchmark hydraulic C-TOWN network (Ostfeld et al., 2011). The hydraulic parameters and quality parameters of the DT model for the water network were calibrated using the Differential Evolution (DE) algorithm. The calibrated DT served as a real-time proxy to generate simulation data, which is used for two different applications in large-scale water networks: (i) Disinfectant dosage regulation task using booster stations and (ii) pipe leakage localization task. The calibrated DT was utilized to estimate the optimal disinfectant dosing rates, ensuring water quality control within an acceptable range using optimization. The results highlight the effectiveness of the neural network and real-time optimization strategy to achieve the optimal dosing rate. For the leakage localization task, the Graph Convolution Networks (GCN) based neural network trained on the DT was found to predict leakage location very accurately.

A Two-Stage Swarm Optimizer With Local Search for Water Distribution Network Optimization.

Evolutionary computation (EC) algorithms have been successfully applied to the small-scale water distribution network (WDN) optimization problem. However, due to the city expansion, the network scale grows at a fast speed so that the efficacy of many current EC algorithms degrades rapidly. To solve the large-scale WDN optimization problem effectively, a two-stage swarm optimizer with local search (TSOL) is proposed in this article. To address the issues caused by the large-scale and multimodal characteristics of the problem, the proposed algorithm divides the optimization process into an exploration stage and an exploitation stage. It first finds a promising region of the search space in the exploration stage. Then, it searches thoroughly in the promising region to obtain the final solution in the exploitation stage. To search effectively the huge search space, we propose an improved level-based learning optimizer and use it in both the exploration and exploitation stages. Two new local search algorithms are proposed to further improve the quality of the solution. Experiments on both synthetic benchmark networks and a real-world network show that the proposed algorithm has outperformed the state-of-the-art metaheuristic algorithms.

Acoustic feature-based leakage event detection in near real-time for large-scale water distribution networks

Abstract Acoustic sensors are widely deployed to detect hidden leakages in water distribution networks (WDNs). However, few studies have been conducted to quantitatively understand the dominant leakage acoustic characteristics, which are usually mixed with unknown environmental noises, coupled with the constraint of sparse deployment of acoustic sensors. In this paper, a comprehensive approach, that performs acoustic data feature analysis, is developed to detect pipe leakages in near real-time via a series of systematic analyses, namely: (1) data quality assessment; (2) features identifications; (3) outlier detection and event classification; and finally (4) near real-time leakage detection. The proposed solution has been tested on two major WDNs in Singapore having around 1,000 km of water pipelines installed with 74 permanently installed hydrophone sensors. The leakage detection results obtained from our case study demonstrate that the dominant leakage acoustic characteristics can be captured in lower intrinsic mode functions (IMFs), to within the frequency range of 100–750 Hz approximately, by decomposing the original acoustic signal. Systemwide leakage event detection and classification models are subsequently trained and tested on acoustic datasets collected over 13 historical months, where more than 70% F1-scores can be obtained from the emulated near real-time leakage detection analysis.

Probabilistic warm solutions-based multi-objective optimization algorithm, application in optimal design of water distribution networks

Single objective optimization of water distribution networks (WDNs) based on construction costs reduces system resiliency. Thus, researchers have developed various optimization frameworks to maximize system resilience while minimizing construction costs during the last decade. They generally utilized population-based meta-heuristic algorithms. These algorithms start with random populations (initial cold solutions), so they need high computational time to converge to the optimal solutions. This paper aims to reduce search space by introducing prebaked initial or warm solutions based on the hydraulic properties of WDNs which are discharge variance and dissipated power function. After generating initial warm solutions, several probabilistic models are used to bring them closer to the global optimal Pareto front and increase their diversity. The proposed approach is used to optimize the design of two well-known benchmarks, Alprovits and Hanoi, and a real-world large-scale WDN. Results show that using warm solutions as the initial population reduces time, the number of iterations, and fitness evaluations to reach a certain accuracy level, more than two times on average for the benchmarks. Also, results show that the proposed method reduces the average construction costs by 45% compared to the traditional optimization method in the same execution time for real-world WDN.

A strategy for sustainable urban water management using water network partitioning with optimal booster pump configuration: A case study

The functionality of water distribution networks (WDNs) plays an important role in preserving human welfare and the sustainable development of cities and society. Like administrative units in urban governance models, a large-scale WDN is partitioned into district metered areas (DMAs) to improve operation and management efficiency. Conventional approaches for water network partitioning (WNP) into DMAs mainly focus on the determination of DMA sizes and configurations (i.e., the clustering phase). They subsequently form a set of boundary pipes to optimally locate flowmeters and gate valves (i.e., the sectorization phase) aimed at minimizing costs and water leakage and improving network reliability. Alternatively, this paper presents a new approach that considers the phasing of the optimal booster pump installation combined with the design of a DMA configuration aimed at optimizing the operation cost and hydraulic performance. The proposed method combines the overall sectorization process to simultaneously optimize the locations of the flowmeters, gate valves, and booster pumps, as well as the pump sizes. The proposed method was applied to a real-world WDN and the results showed two advantages: first, the boundary pipes are good candidate locations for booster pump installation, which makes it possible to improve hydraulic performance; and second, operation costs are reduced when compared to those of the source-connecting central pump configuration. This study provides a methodology to select the most suitable pumping configurations for DMA design/operation in WDNs.

Economic Predictive Control with Periodic Horizon for Water Distribution Networks

This paper deals with control of pumps in large-scale water distribution networks with the aim of minimizing economic costs while satisfying operational constraints. Finding a control algorithm in combination with a model that can be applied in real-time is a challenging problem due to the nonlinearities presented by the pipes and the network sizes. We propose a predictive control algorithm with a periodic horizon. The method provides a way for the economic operation of large water networks with a low-complexity linear model. Economic Predictive control with a periodic horizon and a terminal state constraint is constructed to keep the state trajectories close to an optimal periodic trajectory. Barrier terms are also included in the cost function to prevent constraint violations. The proposed method is tested on a state-of-the-art EPANET model of the water network of a medium size Danish town (Randers) and shown to perform as intended under varying conditions.

An improved hybrid community detection algorithm for partitioning of water distribution networks

District metered areas (DMAs) are widely used by water utilities to manage water distribution networks (WDNs). This study presents a novel methodology that couples the improved hybrid community detection algorithm and combinatorial optimization process for partitioning WDNs into DMAs. In the node clustering phase, the hybrid algorithm based on the improved modularity index enables the fast formation of sufficient partition solutions with a more balanced water demand distribution and reduced diameters of boundary pipes. Then, in the partition dividing phase, a three-step optimization method, comprising preliminary hydraulic analysis, search for a suboptimal solution and multi-objective optimization, is presented to find a fast and optimal solution for the location of flow meters and isolation valves in WDNs. The overall methodology is applied to a large-scale WDN, proving its applicability and superiority in generating engineering partition configurations.

Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks

This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation.

Data-driven leak localization in WDN using pressure sensor and hydraulic information

Maintaining a good quality of service under a wide range of operational management is challenging for water utilities. One of the significant challenges is the location of water leaks in the large-scale water distribution networks (WDN) due to limited data information throughout the system, generally having only flow sensors at the entrance of the system and some pressure sensors in some selected nodes. In addition, most systems do not have a hydraulic model of the network. Therefore, when using the hydraulic model, the presence of model errors such as nodal demand uncertainty and measurement noise can interfere with the performance of the leak location method. This work presents a fully data-driven technique to reduce the area of the leak localization in the WDN, using Graph theory to represent the network. To do so, we have developed a distance clustering with pre-defined centroids that are the sensor pressure information and some selected nodes. Furthermore, some extra pressure information of leaks events in the selected centroids is studied to develop a correlation between the pressure measurement and the event. Finally, the approach is evaluated in real-world water systems and discusses graphical results and key performance indicators.

Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms

In a world where the availability of water is decreasing, its use must be thoroughly optimized. Irrigated agricultural systems, as the main user of the planet's fresh water, must improve its management and save as much of this scarce resource as possible. However, the heterogeneity of these complex systems that are frequently organized in water user associations makes the daily management of this resource difficult. The new information and communication technologies as well as artificial intelligence techniques help to understand the heterogeneity of these complex systems, making it possible to better manage them. However, the implementation of a tool with these characteristics requires a large and heterogeneous amount of data from different sources. Thus, in this work, a new tool for managers based on water demand forecasting at the field scale for the week ahead has been developed. This tool, WatergyForecaster, combines artificial intelligence techniques, satellite remote sensing (Sentinel 2) and open source climate data to automatically build a water forecasting model at the farm scale for a week in advance. WatergyForecaster, developed in Python, was applied to a real water user association (WUA), obtaining a set of optimum models with an accuracy that ranged from 17% to 19% and representativeness higher than 80%.