Search for: All records

The effectiveness of model-based leak localization methods in water distribution systems (WDSs), including optimization-based and machine learning approaches, significantly depends on the quality and quantity of input data. Pressure data, easily accessible due to nonintrusive sensor installation and maintenance, are commonly used. However, economic constraints limit the number of sensors in WDSs, highlighting the need for strategic sensor placement to enhance data quality. This study introduces a novel, method-independent sensor placement strategy that integrates cluster definitions (leak resolution) with intuitive surrogates for localization performance, addressing the limitations of existing methods reliant on complex, nonintuitive metrics. We propose the Euclidean cluster-based optimal placement of sensors (ECOPS) approach, which employs sensitivity and uniqueness as fundamental signal properties to guide sensor placement. Validation tests within a comprehensive real-world WDS demonstrate that ECOPS outperforms existing surrogate-based approaches and improves the performance of current sensors installed for leak characterization. These findings provide compelling evidence of ECOPS’s potential for enhancing pressure sensor placement, thereby improving leak localization in WDS applications. 

The primary goal of the project is to leverage recent developments in smart water technologies to detect and reduce water leakages in large water distribution networks with the aid of neural networks. A cost effective non-invasive solution to detect leakages in transmission pipelines is needed by many water utilities as it will lead to significant water savings and reduced pipe breakage frequencies, especially in older infrastructure systems. The eventual goal of the project is to test the ANN model on a real network using field measured pressure and pipe breakage data after tuning and developing the model with simulated data. In this project we propose building a regression model, based on Multi-Layer Perceptron (MLP) algorithm, which is a class of feedforward Artificial Neural Networks (ANNs) to detect the leak locations within a proposed network. The model should be able to learn the structure, i.e. mapping of various leak nodes and sensor nodes in an area, such that it can detect the leak nodes based on the pressure values with significant accuracy.