首页|Capital Normal University Reports Findings in Machine Learning (Enhancing short- term streamflow prediction in the Haihe River Basin through integrated machine l earning with Lasso)
Capital Normal University Reports Findings in Machine Learning (Enhancing short- term streamflow prediction in the Haihe River Basin through integrated machine l earning with Lasso)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Beijing, People's Repu blic of China, by NewsRx editors, research stated, "With the widespread applicat ion of machine learning in various fields, enhancing its accuracy in hydrologica l forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale strea mflow and explores the application of the Lasso feature selection method alongsi de three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction." Funders for this research include National Key R&D Program of China , National Natural Science Foundation of China. Our news journalists obtained a quote from the research from Capital Normal Univ ersity, "Through comparative experiments, we found that the Lasso method signifi cantly enhances the model's performance, with a respective increase in the gener alization capabilities of the three models by 21, 12, and 14%. Amon g the selected features, lagged streamflow and precipitation play dominant roles , with streamflow closest to the prediction date consistently being the most cru cial feature. In comparison to the TTS and RF models, the LSTM model demonstrate s superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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