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A method to analyze the sensitivity ranking of various abiotic factors to acoustic densities of fishery resources in the surface mixed layer and bottom cold water layer of the coastal area of low latitude: a case study in the northern South China Sea.
Scientific Reports ( IF 4.6 ) Pub Date : 2020-07-07 , DOI: 10.1038/s41598-020-67387-7
Mingshuai Sun 1, 2, 3 , Yancong Cai 1, 2 , Kui Zhang 1, 2 , Xianyong Zhao 4 , Zuozhi Chen 1, 2, 5
Affiliation  

This is an exploratory analysis combining artificial intelligence algorithms, fishery acoustics technology, and a variety of abiotic factors in low-latitude coastal waters. This approach can be used to analyze the sensitivity level between the acoustic density of fishery resources and various abiotic factors in the surface mixed layer (the water layer above the constant thermocline) and the bottom cold water layer (the water layer below the constant thermocline). The fishery acoustic technology is used to obtain the acoustic density of fishery resources in each water layer, which is characterized by Nautical Area Scattering Coefficient values (NASC), and the artificial intelligence algorithm is used to rank the sensitivity of various abiotic factors and NASC values of two water layers, and the grades are classified according to the cumulative contribution percentage. We found that stratified or multidimensional analysis of the sensitivity of abiotic factors is necessary. One factor could have different levels of sensitivity in different water layers, such as temperature, nitrite, water depth, and salinity. Besides, eXtreme Gradient Boosting and random forests models performed better than the linear regression model, with 0.2 to 0.4 greater R2 value. The performance of the models had smaller fluctuations with a larger sample size.



中文翻译:

分析低纬度沿海地区表层混合层和底部冷水层中各种非生物因子对渔业资源声密度的敏感性等级的方法:以南海北部为例。

这是在低纬度沿海水域中结合人工智能算法,渔业声学技术和多种非生物因素的探索性分析。该方法可用于分析渔业资源的声密度与表面混合层(恒温器上方的水层)和底部冷水层(恒温器下方的水层)中各种非生物因素之间的敏感度水平。 。渔业声学技术用于获取每个水层中渔业资源的声密度,其特征在于航海面积散射系数值(NASC),人工智能算法用于对各种非生物因素和NASC值的敏感性进行排序两层水 并且根据累积贡献百分比对等级进行分类。我们发现非生物因素敏感性的分层或多维分析是必要的。一个因素在不同的水层中可能具有不同的灵敏度水平,例如温度,亚硝酸盐,水深和盐度。此外,eXtreme Gradient Boosting和随机森林模型的效果优于线性回归模型,R值提高了0.2至0.42值。模型的性能波动较小,样本量较大。

更新日期:2020-07-07
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