文章基本信息

标题：A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England
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作者：Hossein Hassani ; Emmanuel S. Silva , Marine Combe , Demetra Andreou , Mansi Ghodsi , Mohammad Reza Yeganegi ; Rodolphe E. Gozlan 等
期刊名称：Stats
电子版ISSN：2571-905X
出版年度：2019
卷号：2
期号：1
页码：89-103
DOI：10.3390/stats2010007
出版社：MDPI AG
摘要：Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully.
关键词：biodiversity; conservation; management; policies; non native introduction; forecasting; support vector machines biodiversity ; conservation ; management ; policies ; non native introduction ; forecasting ; support vector machines