首页|Study Findings on Machine Learning Discussed by Researchers at Graduate Universi ty of Advanced Technology (Vulnerability of the rip current phenomenon in marine environments using machine learning models)
Study Findings on Machine Learning Discussed by Researchers at Graduate Universi ty of Advanced Technology (Vulnerability of the rip current phenomenon in marine environments using machine learning models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting from Kerman, I ran, by NewsRx journalists, research stated, "Hidden and perilous rip currents a re one of the primary factors leading to drownings of beach swimmers. By identif ying the coastal areas with the highest likelihood of generating rip currents, i t becomes possible to prevent fatalities and mitigate economic losses associated with these hazardous currents." Financial supporters for this research include National Natural Science Foundati on of China; Graduate University of Advanced Technology; Guangdong Ocean Univers ity. The news journalists obtained a quote from the research from Graduate University of Advanced Technology: "Rip currents are characterized as streams of water mov ing towards the open sea, forming within the area where waves break, due to vari ations in wave-induced radiation stresses and pressure along the coastline. This study utilized nine different Machine Learning (ML) models, including M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Progr amming (GEP), Evolutionary Polynomial Regression (EPR), Random Forest (RF), Supp ort Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Stacked ML models, to estimate the Relative Tide Range (RTR) va lues for 50 southern beaches in China. Through this approach, we gathered a reli able dataset from prior research conducted on the southern coast of China. In th is study, two parameters, namely dimensionless fall velocity parameter (O) and t ide range (TR) are used to predict the vulnerability of rip current event. The r esults of the AI models were assessed by various statistical analyses (Correlati on of Coefficient [R], Root Mean Square Er ror [RMSE], violin diagram, heatmap, and t aylor diagram) for training and testing stages. Accordingly, the MARS model exhi bited superior performance compared to other AI models in accurately predicting the RTR value."
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