Assessing the Fidelity and Utility of Water Systems Data Using Generative Adversarial Networks: A Technical Review

Limited data access to Water Distribution Systems (WDSs) is a longstanding barrier to data-driven research and development. This limited access is further exacerbated by the reluctance of WDSs operators to share data. Driven by the absence of standard mandates, resource constraints, privacy and security concerns, and legal challenges, access to big data has been a challenge in the water scientific community. This review paper addresses this limitation by utilizing Generative Adversarial Networks (GANs) to generate realistic synthetic datasets, overcoming data scarcity and privacy concerns in WDSs. We review, train, and evaluate seven state-of-the-art GAN models using three multivariate time-series datasets. The core contribution of this work lies in its comprehensive technical review of the GANs, comparing and evaluating their ability to replicate temporal dynamics and maintain spatio-temporal dependencies within WDSs. For evaluation, we use techniques like t-distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) to quantify the diversity of the generated synthetic data. Key findings indicate that specific GAN models, such as Cramer GAN and CTGAN, are effective in generating data for predictive modeling, replacing the need for original WDSs datasets. Additionally, DoppelGANger and TimeGAN exhibit strong capabilities in preserving essential spatio-temporal relationships, which are critical for applications like environmental impact estimation. The results also highlight the practical utility of GAN-generated synthetic data in supporting the secure and effective management of WDSs, particularly in scenarios where data are scarce or sensitive. This research contributes to the application of Artificial Intelligence (AI) in water resource management and guides the selection of appropriate GAN models for specific tasks and contexts, demonstrating their practical implications in real-world scenarios. Experimental results are recorded, evaluated, and discussed.