首页|Reports Outline Machine Learning Study Findings from Rzeszow University of Technology (Evaluating the Utility of Selected Ma- chine Learning Models for Predicting Stormwater Levels in Small Streams)
Reports Outline Machine Learning Study Findings from Rzeszow University of Technology (Evaluating the Utility of Selected Ma- chine Learning Models for Predicting Stormwater Levels in Small Streams)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intelligence are discussed in a new report. According to news originating from Rzeszow, Poland, by NewsRx correspondents, research stated, “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 news reporters obtained a quote from the research from Rzeszow University of Technology: “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 (R2 = 0.803-0.980, RMSE = 1.547-4.596) over XGBoost v2.0.3. (R2 = 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.”
Rzeszow University of TechnologyRzeszowPolandEuropeCyborgsEmerging TechnologiesMachine Learning
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