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

Author

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  • Rejeesh Rayaroth

    (Pondicherry Engineering College)

  • Sivaradje G

    (Pondicherry Engineering College)

Abstract

Water Distribution Systems (WDS) are the large scale systems that demand design of enhanced leak detection and isolation techniques to prevent from water waste. Leakages lead to imperative loss of water in water distribution networks. Many works are published on leak detection of WDS. However, the existing methods failed to improve the leakage detection accuracy and reduce the time. In this paper, Random Decision Tree Bagging Classifier based Shuffled Frog Leaping Optimization (RDTBC-SFLO) Technique is introduced. In RDTBC-SFLO, the collected pressure data are taken as training data. Random ID3 decision forest classifier process constructs the ID3 decision tree and produces the classification results of pressure data. After classification, number of nodes at abnormal pressure data is randomly generated as initial population in shuffled frog leaping optimization process. The fitness value of every node is calculated and the optimal nodes are chosen for the sensor placement in WDS with higher accuracy and minimal error rate. The simulation results show that RDTBC-SFLO technique increases the performance of water leakage detection with minimum classification time when compared to state-of-the-art works.

Suggested Citation

  • Rejeesh Rayaroth & Sivaradje G, 2019. "Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3111-3125, July.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02296-7
    DOI: 10.1007/s11269-019-02296-7
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    References listed on IDEAS

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    1. David B. Steffelbauer & Daniela Fuchs-Hanusch, 2016. "Efficient Sensor Placement for Leak Localization Considering Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5517-5533, November.
    2. Huan-Feng Duan, 2018. "Accuracy and Sensitivity Evaluation of TFR Method for Leak Detection in Multiple-Pipeline Water Supply Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(6), pages 2147-2164, April.
    3. Marco Fagiani & Stefano Squartini & Leonardo Gabrielli & Marco Severini & Francesco Piazza, 2016. "A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids," Energies, MDPI, vol. 9(9), pages 1-25, August.
    4. Erfan Hajibandeh & Sara Nazif, 2018. "Pressure Zoning Approach for Leak Detection in Water Distribution Systems Based on a Multi Objective Ant Colony Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2287-2300, May.
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    Cited by:

    1. Guancheng Guo & Xipeng Yu & Shuming Liu & Xiyan Xu & Ziqing Ma & Xiaoting Wang & Yujun Huang & Kate Smith, 2020. "Novel Leakage Detection and Localization Method Based on Line Spectrum Pair and Cubic Interpolation Search," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3895-3911, September.
    2. Zukang Hu & Wenlong Chen & Beqing Chen & Debao Tan & Yu Zhang & Dingtao Shen, 2021. "Robust Hierarchical Sensor Optimization Placement Method for Leak Detection in Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3995-4008, September.

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