CPUE Standardization of Illex angentinus Based on BP Neural Network

Abstract:

Illex angentinus is an important economic cephalopod worldwide, as well as an important fishing target for China’s mainland and Taiwan Province. Catch per unit effort (CPUE) is a widely used index to express stock abundance in fisheries. However, CPUE is susceptible to many factors; therefore, it must be standardized. In this research, the statistical data of squid fishing production and the corresponding environmental data from January to April, 2012 to 2017 in mainland China were selected, and the BP neural network method was adopted to establish a model to standardize CPUE. The model uses month, longitude (Lon), latitude (Lat), sea surface temperature (SST), potential temperature of the -95 m layer (PT95), chlorophyll-a (Chl-a), and sea surface salinity (SSS) as input factors. There were 12 hidden layers, from 4 to 15, and the output layer was CPUE. R2, mean squared error (MSE), and average relative variance (ARV) were used as the evaluation criteria of the model. The results showed that a 7-18-1 structure was the optimal model, and the input layer factors in order from high to low weights were SST, SSS, month, PT95, Lon, Lat, and Chl-a. The temporal and spatial distribution predictions for the same sea area indicated that although the BP neural network model could not accurately predict the specific values of CPUE, it could predict the temporal and spatial variations of CPUE, which could be used for the CPUE standardization of I. angentinus.