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Drinking Water Engineering and Science An interactive open-access journal
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https://doi.org/10.5194/dwes-2018-19
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/dwes-2018-19
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Oct 2018

Research article | 25 Oct 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Drinking Water Engineering and Science (DWES).

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Rejeesh Rayaroth and Sivaradje Gopalakrishnan Rejeesh Rayaroth and Sivaradje Gopalakrishnan
  • Department of Electronics & communication Engineering, Pondicherry Engineering College, Puducherry, 605014, India

Abstract. Water Distribution System distributes the water to customer with the better quality and pressure. Distribution system supplies the water from their source to usage point. Due to the leakage, the sufficient amount of water is not delivered to the consumer. Many researchers introduced the techniques for detecting the water leakage in distribution system. But, the water leakage detection accuracy was not improved and time consumption was also not reduced. To improve the water leakage detection performance, Enhanced BrownBoost Classifier based Glowworm Swarm Optimization (EBBC-GWO) Method is introduced. EBBC-GWO method introduces two models namely, Enhanced BrownBoost Classifier model and Glowworm Swarm Optimization model. Enhanced BrownBoost Classifier model considers k-Nearest Neighbor (k-NN) classifier as weak classifier. It classifies the training samples with neighbor's majority vote for allocating the object to the class. Brownboost classifier combines all k-NN classifier to construct strong classifier. By this way, data are classified as the normal data or abnormal data with higher accuracy. After classification, optimization process is executed where every solution corresponds to the glowworm (i.e., abnormal pressure data node) in search space. Every glowworm has objective function for addressing the optimization problem. Every glowworm operates in probabilistic means to choose the neighbor with higher luciferin value and transmit to it. Glowworm updates its location to the glowworm in dynamic decision space and optimal one is selected for water leakage detection. By this way, water leakage detection accuracy is improved with lesser false positive rate. Experimental evaluation of proposed EBBC-GWO method is carried out with respect to number of pressure data and sensor placement nodes. The results demonstrated that EBBC-GWO method is higher in case of classification accuracy, false positive rate, classification time and water leakage detection accuracy. The simulation results show that EBBC-GWO method increases the performance of water leakage detection accuracy and reduces classification time when compared to state-of-the-art works.

Rejeesh Rayaroth and Sivaradje Gopalakrishnan
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Status: open (until 18 Dec 2018)
Status: open (until 18 Dec 2018)
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Rejeesh Rayaroth and Sivaradje Gopalakrishnan
Rejeesh Rayaroth and Sivaradje Gopalakrishnan
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Short summary
Leaks are major concern for a water distribution network. Leak detection and optimal sensor placement for localisation of leaks are discussed. By increasing the number of sensors leak detection accuracy can be improved but cost of deployment increases. So we have to place optimal number of sensors at optimal places. Classification of water distribution network as normal and abnormal data are done with high accuracy. Optimisation,placing optimal sensors at optimal places also done with high accuracy.
Leaks are major concern for a water distribution network. Leak detection and optimal sensor...
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