Short-term prediction of fishing effort distributions by discovering fishing chronology among trawlers based on VMS dataset

Abstract

Short-term prediction of fishing effort distributions will guide fishery management in a dynamic way. However, it meets two unique challenges: the randomness of fishers’ behaviors and the diversity of marine meteorology such as sea storms in the short period. This study proposes short-term prediction system of fishing effort distribution by mining a new kind of knowledge: fishing chronology among trawlers. We first define, quantify, and dig out chronological fishing relations among trawlers based on the VMS dataset. Then the system extracts the optimal early bird set from chronological fishing relations, whose current fishing behaviors can serve as indicators of future fishing effort distributions. Based on the knowledge of fishing chronology, we further design a Convolution Neural Network (CNN) to predict the short-term fishing effort distribution, only taking the current fishing behaviors of early birds as input. We evaluate the system performance on the VMS dataset of 1589 trawlers in the East China Sea from October 2015 to April 2017. The system uses the VMS traces in the first half period to calculate fishing chronology among trawlers, to extract early birds, and to train the CNN model. The traces in the last half period is used to evaluate the prediction accuracy. The results confirm a low prediction error ratio of 6.95% across all the weeks only by tracking 19 early birds. More importantly, our prediction system keeps its accuracy during the week of a sea storm in Feb. 8th to 10th, 2017. The application of our system for fishery management is encouraging: tracking only 1% trawlers suffices to predict short-term fishing effort distributions in the near future.