Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models ( R 2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. ( R 2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.