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

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.