Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation

Lučin, Ivana; Čarija, Zoran; Družeta, Siniša; Lučin, Bože

doi:10.1109/access.2021.3129703

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…• Sophisticated design and development for various modular hardware and software exclusively for the water distribution system are required. The high-quality sen- Leak Localization [159] Leak location, leak size, and base node demand uncertainty Random forest classifier sors and actuators for water distribution systems are costlier compared to the low-cost embedded computing platforms. IoT solutions require huge number of nodes (may be hundreds and thousands in number), and hence the overall cost for hardware components, internet communication, and data roaming have to be reduced [181].…”

Section: Analysis Of Iot Characteristics and Constraints In The Deplo...mentioning

confidence: 99%

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

et al. 2022

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Water distribution systems are one of the critical infrastructures and major assets of the water utility in a nation. The infrastructure of the distribution systems consists of resources, treatment plants, reservoirs, distribution lines, and consumers. A sustainable water distribution network management has to take care of accessibility, quality, quantity, and reliability of water. As water is becoming a depleting resource for the coming decades, the regulation and accounting of the water in terms of the above four parameters is a critical task. There have been many efforts towards the establishment of a monitoring and controlling framework, capable of automating various stages of the water distribution processes. The current trending technologies such as Information and Communication Technologies (ICT), Internet of Things (IoT), and Artificial Intelligence (AI) have the potential to track this spatially varying network to collect, process, and analyze the water distribution network attributes and events. In this work, we investigate the role and scope of the IoT technologies in different stages of the water distribution systems. Our survey covers the state-of-the-art monitoring and control systems for the water distribution networks, and the status of IoT architectures for water distribution networks. We explore the existing water distribution systems, providing the necessary background information on the current status. This work also presents an IoT Architecture for Intelligent Water Networks -IoTA4IWNet, for real-time monitoring and control of water distribution networks. We believe that to build a robust water distribution network, these components need to be designed and implemented effectively. INDEX TERMS Internet of things, IoT communication technologies, IoT services, water distribution networkThis article has been accepted for publication in IEEE Access.

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Section: Analysis Of Iot Characteristics and Constraints In The Deplo...mentioning

confidence: 99%

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

et al. 2022

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“…It can be divided into hard-margin and soft-margin [9]. When using these methods, one needs to be careful of losing information because it depends on support vector [10]. Support vector machine has many advantages like it can solve small sample problem and handle high dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Subscribing prediction of term deposit based on decision tree, random forest and support vector machine

Chen

2024

ACE

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To classify customers and predict their behaviour based on some of their features and what they do before is most people want to do. This study finds a data set about bank customers from Kaggle and use three different classification models to classify customers and predict whether they will subscribe a term deposit based on some of their features. The three classification models are decision tree model, random forest model and support vector machine model. Firstly, using these models to get the feature importance and accuracy rate to evaluate the result. In addition, this study changes the parameters about these models and find how can get better result. Based on these models to process data set and getting the result, this paper also compared their results and find advantages and disadvantages of three models. Finally, this paper also discusses how to improve the models so that they can get the better result and solve some other problems.

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“…A genetic algorithm-based approach is used to calibrate the WDN's parameters accurately, which helps implement a sensor placement method for leak diagnosis purposes [14,15]. In [16], a machine learning technique is used for leak diagnosis purposes by implementing the Random Forest classifier in which the segmentation of nodes throughout the WDN is performed. Although the leak diagnosis is achieved with success, the computational cost is high, especially for multi-leak scenarios for large-scale WDN.…”

Section: Introductionmentioning

confidence: 99%

Pressure Interpolation in Water Distribution Networks by Using Gaussian Processes: Application to Leak Diagnosis

Liy-González,

Santos-Ruiz,

Delgado-Aguiñaga

et al. 2024

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This work presents the reconstruction of the pressure head map of a water distribution system (WDS). This approach relies on historical data collected from a reduced number of sensors placed at some nodes of the WDS. Thus, a Gaussian regression process is then applied to estimate the pressure head at those nodes without a sensor, which allows the reconstruction of the pressure map for the whole network. Then, for leak diagnosis purposes, a dataset of pressure head maps of the WDN is created considering leaky scenarios, and a correlation method is applied to estimate the leak location. Then, for clarity, the Hanoi network is used to evaluate the performance of this leak diagnosis strategy in a simulation environment, assuming the availability of only three sensors. The results showed the potential for pressure head map reconstruction and leak localization.

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Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation

Cited by 8 publications

References 30 publications

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

Subscribing prediction of term deposit based on decision tree, random forest and support vector machine

Pressure Interpolation in Water Distribution Networks by Using Gaussian Processes: Application to Leak Diagnosis

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