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
作者： Sun, Mingshuai 1 ; Cai, Yancong 1 ; Zhang, Kui 1 ; Zhao, Xianyong 5 ; Chen, Zuozhi 1 ;
作者机构： 1.Chinese Acad Fishery Sci, South China Sea Fisheries Res Inst, Guangzhou 510300, Peoples R China
2.Minist Agr & Rural Affairs, Key Lab Open Sea Fishery Dev, Guangzhou 510300, Peoples R China
3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China
4.Shanghai Ocean Univ, Shanghai 200120, Peoples R China
5.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Qingdao 266237, Peoples R China
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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