Home // IARIA Congress 2023, The 2023 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications // View article

Cod Catch Forecasting through Machine Learning Algorithms at the Haul Level

Authors:
Huamin Ren
Yajie Liu
Keshav Prasad Paudel

Keywords: Machine learning; Big data; Fishing catch; Forecast.

Abstract:
This paper leverages historical fishing data in conjunction with machine learning algorithms to uncover fishing patterns and more precisely forecast fishing catches. The introduction of Machine Learning techniques into the fishing industry holds significant promise for enhancing operational performance. Such methodologies can promote great efficiency and enhance the decision-making processes, optimizing factors such as fishing effort, location, and catch rates. Preliminary results illustrate the efficacy of three distinct machine learning algorithms: Linear Regression, RANdom SAmple Consensus (RANSAC), and Light Gradient Boosting Machine (LightGBM). Throughout our experimentation, it became evident that the modeling performance is profoundly influenced by the sampling strategy. This influence likely stems from inherent noise in the data, which degrades overall performance. Our findings offer insights into the effective employment of machine learning algorithms for data selection and modeling.

Pages: 123 to 127

Copyright: Copyright (c) IARIA, 2023

Publication date: November 13, 2023

Published in: conference

ISBN: 978-1-68558-089-6

Location: Valencia, Spain

Dates: from November 13, 2023 to November 17, 2023