Graph neural networks-based dynamic water quality state estimation in water distribution networks

Salem, Aly K; Taha, Ahmad F; Abokifa, Ahmed A

doi:10.1016/j.engappai.2024.109426

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.

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