HMM을 이용한 어선 활동 예측 기법의 선박패스(V-Pass) 적용

• 영문명: Hidden Markov Model(HMM)-Based Fishing Activity Prediction Using V-Pass Data
• 발행기관: 한국연안방재학회
• 저자명: 박주한(Ju-Han Park) 전호군(Ho-Kun Jeon) 양찬수(Chan-Su Yang)
• 간행물 정보: 『한국연안방재학회지』제8권 제4호, 221~227쪽, 전체 7쪽
• 주제분류: 사회과학 > 사회과학일반
• 파일형태: PDF
• 발행일자: 2021.10.31

영문 초록

Illegal fishing has been a serious threat to the conservation of seafood resources and provoked the importance of marine surveillance. There are several types of fishing vessel monitoring systems operated by Republic of Korea, for example, Vessel Monitoring System(VMS), Automatic Identification System (AIS), V-Pass and VHF-DSC. However, those methods are not adaptable directly to fishing activity monitoring. The limitation requires more human resources to determine fishing status. Thus, this study proposes a method of estimating fishing activity from V-Pass, fishing vessel position reporting system, using Hidden Markov Model (HMM). HMM is a model to determine status through probability distribution for a sequence of time-series data. First of all, fishing activity status was labeled on V-Pass data. The distribution of speed on fishing activity was computed from the labeled data and HMM was constructed from the data obtained at Socheongcho Ocean Research Station (SORS). The model was first applied to the data of SORS for a test, and then Busan for validation. The model showed 99.4% and 89.6% as test and validation accuracy, respectively. It is concluded that the HMM can be applicable to predict a fishing activity from vessel tracks.

목차

1. Introduction

2. Area & Data

3. Methodology

4. Result

5. Conclusion and Future Work

키워드

Fishing Activity Hidden Markov Model V-Pass