Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS

Rayaroth, Rejeesh; Gopalakrishnan, S.

doi:10.1007/s11269-019-02296-7

Cited by 20 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tian et al [103] utilized cluster analysis to identify the contributions of mixed water sources, such as aluminum migration and sea-sonal variations, to aluminum residues in urban drinking-water supply systems. Rayaroth et al [104] introduced a random decision tree bagging classifier using the shuffling frogleaping optimization method to detect water leaks in distribution networks with optimal sensor placement. The service life of pipelines, crucial for water supply management, was found to be influenced by residual chlorine, and an advanced meta-learning model based on a neural network was proposed by Almheiri et al [105].…”

Section: Application In Drinking Watermentioning

confidence: 99%

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Alprol,

Mansour,

Ibrahim

et al. 2024

Water

View full text Add to dashboard Cite

Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water quality prediction has the potential to revolutionize traditional approaches and address urgent challenges, considering the global demand for clean water and sustainable systems. This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas. A successful example is the implementation of an IoT-based automated water quality monitoring system that utilizes cloud computing and ML methods to effectively address the above-mentioned issues. The IoT has been employed to optimize, simulate, and automate various aspects, such as monitoring and managing natural systems, water-treatment processes, wastewater-treatment applications, and water-related agricultural practices like hydroponics and aquaponics. This review presents a collection of significant water-based applications, which have been combined with the IoT, artificial neural networks, or ML and have undergone critical peer-reviewed assessment. These applications encompass chlorination, adsorption, membrane filtration, monitoring water quality indices, modeling water quality parameters, monitoring river levels, and automating/monitoring effluent wastewater treatment in aquaculture systems. Additionally, this review provides an overview of the IoT and discusses potential future applications, along with examples of how their algorithms have been utilized to evaluate the quality of treated water in diverse aquatic environments.

show abstract

Section: Application In Drinking Watermentioning

confidence: 99%

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Alprol,

Mansour,

Ibrahim

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…Prior studies have examined optimization methodologies for damage detection in pipeline systems. [17][18][19] In the OSP, evolutionary algorithms have advantages in terms of modification, since the quantity of sensors and candidate positions are formulated as a problem. 16 Modified evolutionary algorithms, that is, problemtailored algorithms, can enhance the optimization performance for a specific problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

Kim

Jung

et al. 2021

Structural Health Monitoring

View full text Add to dashboard Cite

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

show abstract

“…Substantial progress is now being made in the development and implementation of intelligent water meters (digital water metering) based on an electronic core [ 5 ] and different communication systems to perform remote accurate readings [ 6 ]. Several studies have focused on the benefits of these meters [ 7 ], which can be improved with a complementary set of pressure sensors located in optimal positions [ 8 , 9 ] to detect and locate leaks [ 10 , 11 ]. These sensor networks have led to the development of algorithms for real-time leak detection [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks

Pérez-Padillo

Morillo

Ramírez-Faz

et al. 2020

Sensors

View full text Add to dashboard Cite

Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time.

show abstract

Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS

Cited by 20 publications

References 22 publications

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks

Contact Info

Product

Resources

About