density-based clustering

Water supply networks are directly related to the economic and social development of urban communities. Due to the rapid increase in population and urbanization, these networks should be properly maintained and rehabilitated. In this paper, an unsupervised learning method with OPTICS density-based clustering algorithm is used to determine the priority areas for rehabilitation. 361 burst event data in one of the Mashhad water network zones were analyzed. The algorithm identified a total number of 16 clusters at 5 different levels of importance. These 16 clusters, which are considered sub-clusters for higher levels, were compared in terms of burst rate. Three clusters with failure rates of 79.1, 62.2, and 50.1 (failures/100 km/year) were introduced as the main priorities for rehabilitation and renovation, respectively. The average burst rate in the whole network was 14.8. The identified clusters were examined in terms of pipe material and diameter and their most affecting factors on the burst rate. The obtained results showed that the OPTICS clustering algorithm has a significant ability to determine priority zones for the rehabilitation plan, by identifying multiple clusters and their according levels. Therefore, the proposed method can be used as a practical and flexible tool to prioritize the rehabilitation process of water networks and identify the main causes of failure events, avoiding complex computational methods or the personal judgment of experts.

Anomaly or outlier detection of flow data in water distribution networks (WDNs) is implemented in data preparation and prepossessing step to achieve reliable historical data; it is important to improve the leakage assessment and management methods and the operations of the network efficiently. The main objective of this paper is to develop a new methodology based on unsupervised learning methods for anomaly or outlier detection in a flow data set in WDNs. The developed methodology includes three steps 1- required data acquisition, 2- data validation and normalization, and 3- anomaly or outlier detection using the density-based spatial clustering of application with noise (DBSCAN) algorithm. The proposed methodology is applied for inflow data into an area in Tehran's urban water distribution network with 15-min sampling intervals for 1394. The results showed that the developed methodology is capable to the detection anomalies due to different type of pipe breaks and unusual legitimate consumption such as water usage due to changes in water consumption pattern or unauthorized consumption. Therefore, this methodology can be used as an applicable and flexible tool for monitoring flow data and detecting and eliminating of different types of outliers from them.