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

文献类型： 外文期刊

第一作者： Sun, Mingshuai

作者： Sun, Mingshuai;Cai, Yancong;Zhang, Kui;Chen, Zuozhi;Sun, Mingshuai;Cai, Yancong;Zhang, Kui;Chen, Zuozhi;Chen, Zuozhi;Sun, Mingshuai;Zhao, Xianyong

作者机构：

期刊名称：SCIENTIFIC REPORTS （ 影响因子：4.379； 五年影响因子：5.133 ）

ISSN： 2045-2322

年卷期： 2020 年 10 卷 1 期

页码：

收录情况： SCI

摘要： 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 R-2 value. The performance of the models had smaller fluctuations with a larger sample size.

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