Conference Agenda

Machine learning is widely used in urban drainage networks, and especially opaque models like the random forest, neural networks, or their variations are frequently used. Opaque models employ thousands of model parameters to model complex relationships in data, which limits the comprehensibility of their results for humans. In this regard, the European “Artificial Intelligence Act” was published in July 2024, specifying a transparent implementation of ML, especially in high-risk areas like critical infrastructure. To address this issue, two trained opaque XGBoost models of previous work are extended with different techniques of post-hoc explainability to understand the model’s decisions. The analysis reveals that the most important input features for both XGBoost models represent the engineered classification into dry or wet flow. The next most important input features for the purely data-driven ML model are related to historical rainfall data. The complex relationship between water flow and historical rainfall data is captured, and high quality data and processing is required to prevent the model from learning incorrect correlations. In contrast, the physics-leveraged ML model includes the output of a hydrodynamic model as an important input feature in the model’s decision, decreasing the influence of historical rainfall data on the model’s decision.