首页|Studies from Brunel University London Update Current Data on Machine Learning (A Comparative Analysis of Advanced Machine Learning Techniques for River Streamfl ow Time-Series Forecasting)
Studies from Brunel University London Update Current Data on Machine Learning (A Comparative Analysis of Advanced Machine Learning Techniques for River Streamfl ow Time-Series Forecasting)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news originating from Uxbridge, United King dom, by NewsRx correspondents, research stated, "This study examines the contrib ution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin." Financial supporters for this research include Horizon Europe. The news editors obtained a quote from the research from Brunel University Londo n: "Different sets of scenarios included rainfall data from different weather st ations located in various geographical locations with respect to the flow monito ring station. Long short-term memory (LSTM)-based models were used to examine th e contribution of rainfall data on streamflow-forecasting performance by investi gating five scenarios whereby RF data from different weather stations were incor porated depending on their geographical positions. Specifically, the All-RF scen ario included all rainfall data collected at 11 stations; Upstream-RF (Up-RF) an d Downstream-RF (Down-RF) included only the rainfall data measured upstream and downstream of the streamflow-measuring station; Pearson-RF (P-RF) only included the rainfall data exhibiting the highest level of correlation with the streamflo w data, and the Flow-only (FO) scenario included streamflow data. The evaluation metrics used to quantitively assess the performance of the models included the RMSE, MAE, and the coefficient of determination, R2. Both ML models performed be st in the FO scenario, which shows that the diversity of input features (hydrolo gical and meteorological data) did not improve the predictive accuracy regardles s of the positions of the weather stations."
Brunel University LondonUxbridgeUnit ed KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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