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

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  • Mariusz Starzec

    (Department of Infrastructure and Water Management, Rzeszow University of Technology, al. Powstańców Warszawy 6, 35-959 Rzeszow, Poland)

  • Sabina Kordana-Obuch

    (Department of Infrastructure and Water Management, Rzeszow University of Technology, al. Powstańców Warszawy 6, 35-959 Rzeszow, Poland)

Abstract

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.

Suggested Citation

  • Mariusz Starzec & Sabina Kordana-Obuch, 2024. "Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams," Sustainability, MDPI, vol. 16(2), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:783-:d:1320430
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    References listed on IDEAS

    as
    1. Mariusz Starzec & Sabina Kordana-Obuch & Daniel Słyś, 2023. "Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area," Sustainability, MDPI, vol. 15(10), pages 1-43, May.
    2. Bikram Manandhar & Shenghui Cui & Lihong Wang & Sabita Shrestha, 2023. "Urban Flood Hazard Assessment and Management Practices in South Asia: A Review," Land, MDPI, vol. 12(3), pages 1-29, March.
    3. Łukasz Amanowicz & Katarzyna Ratajczak & Edyta Dudkiewicz, 2023. "Recent Advancements in Ventilation Systems Used to Decrease Energy Consumption in Buildings—Literature Review," Energies, MDPI, vol. 16(4), pages 1-39, February.
    4. Halit Enes Aydin & Muzaffer Can Iban, 2023. "Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2957-2991, April.
    5. Huan Xu & Ying Wang & Xiaoran Fu & Dong Wang & Qinghua Luan, 2023. "Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China," IJERPH, MDPI, vol. 20(5), pages 1-23, March.
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