Extreme Events and Dam Safety: Machine Learning Approach to Predict Spillway Erosion

This study examines the erosion potential of earthen spillways under the growing risks posed by changing climate and extreme flood events, which threaten the stability and safety of dam infrastructure. Specifically, it employs a machine learning approach to evaluate how readily available spillway width and stream power can predict erosion potential. Site-specific erosion prediction methods are often costly and time-consuming because they rely on extensive field investigations and physical modeling. To address these challenges, this research employs multiple machine learning algorithms, including logistic regression, Support Vector Machine, and Random Forest, on existing data to classify spillways as erodible or non-erodible cases. The Random Forest model demonstrated the best predictive performance, achieving 82.7% accuracy on the test dataset. To further interpret the reliability of model predictions, a Bayesian probability analysis was performed, revealing that when the model predicts erosion, there is a 59% probability that the dam will actually experience erosion. These results highlight how integrating existing datasets with machine learning and probabilistic reasoning can enhance dam safety assessment by considering the accuracy, efficiency, and reliability of spillway erosion predictions.