Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Leakage Identification in Water Distribution Networks Based on XGBoost Algorithm
https://orcid.org/0000-0002-7782-7781
To detect leakage in urban water distribution networks and study the relationship between monitoring information and leakage diagnosis, the XGBoost algorithm was applied to identify the leakage zone and predict the leakage level. Software was adopted to call EPANET V2.2 for analyzing a water distribution network model, and emitters were added in the middle of pipes to simulate leakage events. By changing the discharge coefficient, the leakage flow was varied in hydraulic analysis. The node pressure sensitivity matrix was calculated, and the sensor placement and pipe zones were determined using the fuzzy c-means clustering method. Different leakage scenarios were simulated, and the location and level of leakage were identified through pressure changes at monitoring points based on the XGBoost algorithm. Taking two hydraulic models of water distribution network as examples to simulate and predict, which were compared with back-propagation neural network algorithm, it was revealed that the XGBoost algorithm can not only identify the leakage zone, but also predict the leakage level well. On the basis of sensor placement by Fuzzy c-means algorithm, for different leak scenarios, the average identification accuracy of the XGBoost algorithm was 5.54% higher than that of the back-propagation neural network algorithm in leakage zone. The average prediction accuracy of the XGBoost algorithm was 2.71% higher than that of the back-propagation neural network algorithm in leakage level. The XGBoost algorithm effectively can identify the leakage of water distribution networks.
Leakage Identification in Water Distribution Networks Based on XGBoost Algorithm
https://orcid.org/0000-0002-7782-7781
To detect leakage in urban water distribution networks and study the relationship between monitoring information and leakage diagnosis, the XGBoost algorithm was applied to identify the leakage zone and predict the leakage level. Software was adopted to call EPANET V2.2 for analyzing a water distribution network model, and emitters were added in the middle of pipes to simulate leakage events. By changing the discharge coefficient, the leakage flow was varied in hydraulic analysis. The node pressure sensitivity matrix was calculated, and the sensor placement and pipe zones were determined using the fuzzy c-means clustering method. Different leakage scenarios were simulated, and the location and level of leakage were identified through pressure changes at monitoring points based on the XGBoost algorithm. Taking two hydraulic models of water distribution network as examples to simulate and predict, which were compared with back-propagation neural network algorithm, it was revealed that the XGBoost algorithm can not only identify the leakage zone, but also predict the leakage level well. On the basis of sensor placement by Fuzzy c-means algorithm, for different leak scenarios, the average identification accuracy of the XGBoost algorithm was 5.54% higher than that of the back-propagation neural network algorithm in leakage zone. The average prediction accuracy of the XGBoost algorithm was 2.71% higher than that of the back-propagation neural network algorithm in leakage level. The XGBoost algorithm effectively can identify the leakage of water distribution networks.
Leakage Identification in Water Distribution Networks Based on XGBoost Algorithm
https://orcid.org/0000-0002-7782-7781
To detect leakage in urban water distribution networks and study the relationship between monitoring information and leakage diagnosis, the XGBoost algorithm was applied to identify the leakage zone and predict the leakage level. Software was adopted to call EPANET V2.2 for analyzing a water distribution network model, and emitters were added in the middle of pipes to simulate leakage events. By changing the discharge coefficient, the leakage flow was varied in hydraulic analysis. The node pressure sensitivity matrix was calculated, and the sensor placement and pipe zones were determined using the fuzzy c-means clustering method. Different leakage scenarios were simulated, and the location and level of leakage were identified through pressure changes at monitoring points based on the XGBoost algorithm. Taking two hydraulic models of water distribution network as examples to simulate and predict, which were compared with back-propagation neural network algorithm, it was revealed that the XGBoost algorithm can not only identify the leakage zone, but also predict the leakage level well. On the basis of sensor placement by Fuzzy c-means algorithm, for different leak scenarios, the average identification accuracy of the XGBoost algorithm was 5.54% higher than that of the back-propagation neural network algorithm in leakage zone. The average prediction accuracy of the XGBoost algorithm was 2.71% higher than that of the back-propagation neural network algorithm in leakage level. The XGBoost algorithm effectively can identify the leakage of water distribution networks.
Leakage Identification in Water Distribution Networks Based on XGBoost Algorithm
J. Water Resour. Plann. Manage.
Wu, Jiajia (Autor:in) / Ma, Donghui (Autor:in) / Wang, Wei (Autor:in)
01.03.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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