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A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply

Author

Listed:
  • Xiaoqin Li

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Department of Irrigation and Drainage, China Institute of Water Resources and Hydropower Research, Beijing 100048, China)

  • Xiaomei Wu

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Department of Irrigation and Drainage, China Institute of Water Resources and Hydropower Research, Beijing 100048, China)

  • Mingzhuang Sun

    (School of Environment, Tsinghua University, Beijing 100084, China)

  • Shengqiao Yang

    (School of Energy and Environmental Engineering, Hebei University of Engineering, Handan 056038, China)

  • Weikun Song

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Department of Irrigation and Drainage, China Institute of Water Resources and Hydropower Research, Beijing 100048, China)

Abstract

Leakage occurs in rural water supply pipelines very often and its locating is quite demanding even for specialists, which could result in a poor operation efficiency of rural water supply projects and thus a low rural water supply guarantee rate. In view of this problem, the detection of leakage, as well as its prediction, is of great significance for the operation, maintenance, and administration of rural water supply projects. The traditional monitoring-warning systems for urban water distribution networks cannot be applied to rural water distribution networks, due to various limitations. Meanwhile, as with the traditional models, most new approaches based on machine learning such as the artificial neural network (ANN), probabilistic neural network (PNN), and statistical learning theory (SLT) do not fit rural water distribution networks much better, being unable to converge and force high-accuracy results with small sample sizes, which is a crucial demand to meet when dealing with rural water supply pipelines. Extreme gradient boosting (XGBoost), a model that specializes in small sample sizes and has a high generalization ability, was applied to a rural water supply project in Ningxia, China. In this study, a monitoring-warning system featuring both leakage locating and quantity estimation was established based on XGBoost. The accuracy and F1-score of the leakage locating model were 95% and 93%, respectively, while those of the leakage quantity model reached 96% and 97%, respectively. Furthermore, the pressure of monitoring stations could be obtained through the feature importance analysis enabled by XGBoost, which is essential for leakage warning. These results indicate that this system based on XGBoost could be a promising solution to the leakage issue in rural water supply projects, as a great inspiration for future developments in intelligent monitoring-warning systems, thus providing reliable approaches for the sustainable development of rural drinking water supply projects.

Suggested Citation

  • Xiaoqin Li & Xiaomei Wu & Mingzhuang Sun & Shengqiao Yang & Weikun Song, 2022. "A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6079-:d:817390
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Omar Hamdy & Hanan Gaber & Mohamed S. Abdalzaher & Mahmoud Elhadidy, 2022. "Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    2. Andrés Ortega-Ballesteros & David Muñoz-Rodríguez & Alberto-Jesus Perea-Moreno, 2022. "Advances in Leakage Control and Energy Consumption Optimization in Drinking Water Distribution Networks," Energies, MDPI, vol. 15(15), pages 1-5, July.

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