Revolutionizing Flood and CSO Management with Advanced Machine Learning Surrogate Models

Flood management and combined sewer overflow (CSO) predictions are critical for safeguarding communities and the environment. This study introduces two revolutionary surrogate models powered by advanced machine learning for Autodesk InfoDrainage and InfoWorks ICM software. The first model, targeting flood management and drainage design, employs a Bayesian convolutional neural network (B-CNN) trained on extensive InfoDrainage 2D hydraulic simulations. It predicts the spatial distribution of water following rainfall events at given points in time within milliseconds, offering speed and accuracy. By integrating physical data, it identifies flow patterns and forecasts water depth during the construction phase, understanding the impact of surface structures like roads and airports in real-time. This technology serves as an optimal flood management tool and a real-time forecasting tool, providing advanced warnings and enabling proactive measures. It achieves global applicability through restricted network and boundary parameterization. The second model predicts CSOs, essential for environmental protection and regulatory compliance. Using the InfoWorks ICM simulator and a unique deep neural network architecture, this machine learning workflow builds a surrogate model that can quickly and accurately forecast the temporal distribution of water at given points in space for various rainfall profiles. Achieving the same accuracy as traditional simulators but in under a minute, it allows for timely interventions, mitigating untreated sewage release and preventing water pollution, health hazards, and ecosystem damage. It can represent any degree of network/boundary complexity but requires site-specific training. Both models highlight the potential of machine learning-based surrogate models to enhance decision-making, reduce risks, and protect communities and the environment from the impacts of flooding and CSOs.