<ul><li>Climate change increases the frequency of extreme rainfall, straining urban infrastructures, especially Combined Sewer Systems (CSS).</li><li>Machine Learning (ML) offers cost-efficient alternatives to traditional physics-based models for urban wastewater management.</li><li>Neural Network architectures are evaluated for CSS time series forecasting, considering predictive performance, model complexity, and resilience to perturbations.</li><li>Local models provide sufficient resilience in decentralized scenarios, ensuring robust modeling of urban infrastructure for sustainable wastewater management.</li></ul>

Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience

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