Robotics & Machine Learning Daily News2024,Issue(Mar.7) :56-56.

New Machine Learning Research from Shandong University of Science and Technology Outlined [Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning]

Robotics & Machine Learning Daily News2024,Issue(Mar.7) :56-56.

New Machine Learning Research from Shandong University of Science and Technology Outlined [Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning]

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Qingdao, People's Republic of China, by NewsRx editors, the research stated, "The soft coral order Alcyonac ea is a common coral found in the deep sea and plays a crucial role in the deep- sea ecosystem." Funders for this research include National Natural Science Foundation of China; Mnr Key Laboratory of Eco-environmental Science And Technology, China; Shandong Provincial Natural Science Foundation; Key Research And Development Program of S handong Province; 801 Institute of Hydrogeology And Engineering Geology; Shandon g Institute of Chinese Engineering S&T Strategy For Development. Our news correspondents obtained a quote from the research from Shandong Univers ity of Science and Technology: "This study aims to predict the distribution of A lcyonacea in the western Pacific Ocean using four machine learning-based species distribution models. The performance of these models is also evaluated. The res ults indicate a high consistency among the prediction results of the different m odels. The soft coral order is primarily distributed in the Thousand Islands Bas in, Japan Trench, and Thousand Islands Trench. Water depth and silicate content are identified as important environmental factors influencing the distribution o f Alcyonacea. The RF, Maxent, and XGBoost models demonstrate high accuracies, wi th the RF model exhibiting the highest prediction accuracy."

Key words

Shandong University of Science and Techn ology/Qingdao/People's Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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