首页|Reports from Kyungpook National University Advance Knowledge in Machine Learning (Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Est imation: A Multi-Model Machine Learning Approach with Particle Swarm Optimizatio n)
Reports from Kyungpook National University Advance Knowledge in Machine Learning (Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Est imation: A Multi-Model Machine Learning Approach with Particle Swarm Optimizatio n)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in artificial intelligence. According to news reportingout of Sangju, South Korea, by NewsRx editors, research stated, “This study presents a comprehensivemulti- model machine learning (ML) approach to predict river bed load, addressing the c hallenge ofquantifying predictive uncertainty in fluvial geomorphology. Six ML models-random forest (RF), categoricalboosting (CAT), extra tree regression (ET R), gradient boosting machine (GBM), Bayesian regression model(BRM), and K-near est neighbors (KNNs)-were thoroughly evaluated across several performance metric slike root mean square error (RMSE), and correlation coefficient ®.”
Kyungpook National UniversitySangjuS outh KoreaCyborgsEmerging TechnologiesMachine LearningParticle Swarm O ptimization
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