About this Environmental Science article

Estimation of the spatiotemporal distribution of fish and fishing grounds from surveillance information using machine learning: The case of short mackerel (Rastrelliger brachysoma) in the Andaman Sea, Thailand by Chonlada Meeanan; Pavarot Noranarttragoon; Piyachoke Sinanun; Yuki Takahashi; Methee Kaewnern; Takashi Fritz Matsuishi is a Environmental Science article available to read on EtoBox.

Rapid and accurate data gathering on fish catches and the geographical distribution of fishing activities is essential in fish stock assessments and fisheries management. Surveillance systems like the Vessel Monitoring System (VMS) have become popular globally, providing accurate data on fishing activities and fish abundance in near real-time. Our study demonstrates a procedure to find the best method in machine learning algorithms for detecting fishing operations from surveillance data. VMS data of 3,237 trips conducted by 91 purse seiners targeting short mackerel (Rastrelliger brachysoma) in Thai waters of the Andaman Sea during 2020 were used for the study. Twenty-five spatiotemporal, environmental, and fisheries-related parameters were investigated as predictors to detect and map the fishing activity. Eight algorithms were used to compare the model performance using cross-validation. The total catch per trip was reallocated to the predicted fishing locations of the trip from the best algorithms to depict the fishing ground. The results show that operation time and vessel speed are essential elements for determining the fishing operation of purse seiners. The most-relevant predi

Author

Chonlada Meeanan; Pavarot Noranarttragoon; Piyachoke Sinanun; Yuki Takahashi; Methee Kaewnern; Takashi Fritz Matsuishi

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)