With the advent of remote sensing, AI, cloud computing, and advanced data storage technologies, there is now an unprecedented opportunity for real-time monitoring of scour around bridge piers. These technologies also enable AI to identify underlying patterns that can predict future events. Pioneering efforts in AI-based real-time scour forecasting were led by Yousefpour et al. (2021, 2023, 2024), utilizing several years of continuous bed and flow elevation data from multiple bridges across the US. Promising results have been achieved using Long Short-Term Memory Networks and Convolutional Neural Networks to predict upcoming scour at bridge piers. However, the accuracy and reliability of these techniques, particularly for forecasting and early warning under extreme flooding conditions, remain areas of active research. Additionally, the performance of AI models varies across bridges with different scour and flow conditions. This paper compares forecasting accuracy across various case studies in different US states, discusses the key merits and limitations of AI-based early warning systems, and explores the challenges of implementing this technology.

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