Leak Location In Water Distribution Networks Research Articles

A fault-injection-based approach to leak localization in water distribution networks using an ensemble model of Bayesian classifiers

Leak localization in water distribution networks (WDNs) is essential to water management systems. Developing a reliable and robust leak localization technique is crucial for reducing water losses in large-scale WDNs. The challenge of leak localization in WDNs can be addressed using a model-based data-driven approach. Each node of a WDN is used as a category label by the classifier model for identifying leakages. As a result of the small number of sensors in a network and the uncertainties associated with the model parameters, the characteristics of some classes may be similar. Consequently, the algorithm's accuracy decreases. The method proposed in this paper uses the concept of fault injection and propagation in digital systems to separate the leak characteristics of the classes. This separation is done by creating a second leak with a known amount and location to propagate the effect of the first leak on the sensors. Various data sets are generated for each time sample by considering different values for the second leak. Finally, an ensemble of Bayesian classifiers simultaneously processes this data set to determine the leak's location. The accuracy of the proposed method has been evaluated using Hanoi and Modena network benchmarks. Experimental results show that the proposed method has 94.6% and 93.76 accuracy without using a time horizon technique, in the presence of all uncertainties for the Hanoi and Modena networks respectively which show more accurate results than existing methods‎.

A microservice architecture for leak localization in water distribution networks using hybrid AI

Abstract Up to 30% of all drinking water is wasted due to leaks in water distribution networks (WDNs). In times of drought and water shortage, wasting so much drinking water has a considerable environmental and financial cost. In this paper, we present a microservice architecture for leak localization in WDNs, where heterogeneous sources of data consisting of sensor measurements, Geographic Information System (GIS), and Customer Relationship Management (CRM) data are used to feed a leak monitoring solution which combines hybrid data-driven and model-based leak detection and localization methodologies. The solution is validated using in-field leak experiments in an operational WDN. The final leak probabilities are presented in a visualization dashboard. The search zone for most leaks is reduced to a few kilometers or less. For other leaks, the solution is able to indicate a larger search zone to reflect its higher leak prediction uncertainty.

Data-driven leak localization in WDN using pressure sensor and hydraulic information

Maintaining a good quality of service under a wide range of operational management is challenging for water utilities. One of the significant challenges is the location of water leaks in the large-scale water distribution networks (WDN) due to limited data information throughout the system, generally having only flow sensors at the entrance of the system and some pressure sensors in some selected nodes. In addition, most systems do not have a hydraulic model of the network. Therefore, when using the hydraulic model, the presence of model errors such as nodal demand uncertainty and measurement noise can interfere with the performance of the leak location method. This work presents a fully data-driven technique to reduce the area of the leak localization in the WDN, using Graph theory to represent the network. To do so, we have developed a distance clustering with pre-defined centroids that are the sensor pressure information and some selected nodes. Furthermore, some extra pressure information of leaks events in the selected centroids is studied to develop a correlation between the pressure measurement and the event. Finally, the approach is evaluated in real-world water systems and discusses graphical results and key performance indicators.

Leak Localization Method for Water-Distribution Networks Using a Data-Driven Model and Dempster–Shafer Reasoning

This article presents a new data-driven method for leak localization in water-distribution networks (WDNs). The method uses the information provided by a set of pressure sensors installed in some internal network nodes in addition to flow and pressure measurements from inlet nodes. Pressure measurements are recorded under leak-free network operation, and a WDN data-driven model of the pressure at each sensed node is adjusted. The pressure estimation from this model is complemented by a Kriging spatial interpolation technique to estimate the pressure in the nodes that are not sensed, leading to a pressure reference map. Leak localization is based on the comparison of this reference pressure map with the current pressure map that is obtained by applying Kriging directly to the pressure measurements provided by sensors. The key element in this comparison is the use of the Dempster–Shafer theory for reasoning under uncertainty. The successful application of the proposed methodology to two real-data case studies is presented.

Optimal Sensor Placement for Leak Location in Water Distribution Networks using Evolutionary Algorithms

In this paper, a sensor placement approach to improve the leak location in waterdistribution networks is proposed when the leak signature space (LSS) method is used.The sensor placement problem is formulated as an integer optimization problem where thecriterion to be minimized is the number of overlapping signature domains computed fromthe original LSS representation. First, a semi-exhaustive search approach based on a lazyevaluation mechanism ensures optimal placement in the case of low complexity scenarios.For more complex cases, a stochastic optimization process is proposed, based on eitherthe genetic algorithms (GAs) or particle swarm optimization (PSO). Experiments on twodifferent networks are used to evaluate the performance of the resolution methods, as well asthe efficiency achieved in the leak location when using the sensor placement results.