首页|Ocean University of China Reports Findings in Machine Learning (Review of machin e learning methods for sea level change modeling and prediction)
Ocean University of China Reports Findings in Machine Learning (Review of machin e learning methods for sea level change modeling and prediction)
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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 Qingdao, People's Repu blic of China, by NewsRx editors, research stated, "Sea level change, a major co nsequence of climate change, presents significant threats to coastal regions and demands precise, timely forecasting for effective management and adaptation. Th is review assesses methodologies and approaches essential for developing robust machine learning (ML) models for predicting and forecasting sea level change (SL C)." Our news journalists obtained a quote from the research from the Ocean University of China, "Key findings reveal that artificial neural networks (ANNs), especia lly deep learning models and their hybrid variants, outperform traditional regre ssion and simpler ML techniques in short-term sea level anomaly prediction. Supe rvised learning approaches dominate the field, while semi-supervised methods exc el in short-term projections. Simpler models, such as regressions and support ve ctor machines perform better with sufficient training data, however, often exhib it lower accuracy in handling complex, non-linear scenarios. The selection of re levant input variables, such as atmospheric, oceanic, and geological factors, si gnificantly influences model accuracy, and the balance between training and test ing data is crucial for avoiding overfitting and underfitting. This review also clarifies the distinction between ML prediction and forecasting as used in the l iterature."
QingdaoPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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