期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability
1
作者 Nandhi Kesavan latha 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期327-337,共11页
Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecologic... Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future. 展开更多
关键词 Machine learning climate change decision support system multiple regression CONSERVATION area receiver operating curve
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部