Integration of satellite-based environmental data for Skipjack tuna fishing ground determination

Adillah Alfatinah, Hone Jay Chu

研究成果: Paper

摘要

Skipjack Tuna fishing ground area was studied using satellite remotely sensed environment and catch data. Skipjack Tuna tend to aggregate in ocean areas that exhibit specific environmental conditions with water quality parameters such as Sea Surface Temperature (SST) and dissolved oxygen. Weekly resolved remotely sensed SST, surface chlorophyll (Chl-a), and Sea Surface Height (SSH) in 2017 being used as the environmental parameters to determine Skipjack tuna fishing ground. Machine Learning method which is Decision Tree (DT) were constructed with the environmental parameters as model covariates to examine Skipjack Tuna fishing ground determination. As a comparison, Generalized Linear Model (GLM) also was applied. This study exhibits the ability of both model performances for the fishing ground determination. Both models appear to be appropriate for fishing ground determination, while the DT acquires a better performance than GLM. The mean accuracy and Area Under Curve (AUC) of DT was about 0.876 and 0.877, respectively. In other hand, GLM only able to acquire 0.6907 and 0.8397 for its mean accuracy and AUC, respectively.

原文English
出版狀態Published - 2020 一月 1
事件40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019 - Daejeon, Korea, Republic of
持續時間: 2019 十月 142019 十月 18

Conference

Conference40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019
國家Korea, Republic of
城市Daejeon
期間19-10-1419-10-18

All Science Journal Classification (ASJC) codes

  • Information Systems

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  • 引用此

    Alfatinah, A., & Chu, H. J. (2020). Integration of satellite-based environmental data for Skipjack tuna fishing ground determination. 論文發表於 40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019, Daejeon, Korea, Republic of.