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http://dx.doi.org/10.7780/kjrs.2020.36.1.4

An Artificial Intelligence Method for the Prediction of Near- and Off-Shore Fish Catch Using Satellite and Numerical Model Data  

Yoon, You-Jeong (Department of Spatial Information Engineering, Pukyong National University)
Cho, Subin (Department of Spatial Information Engineering, Pukyong National University)
Kim, Seoyeon (Department of Spatial Information Engineering, Pukyong National University)
Kim, Nari (Geomatics Research Institute, Pukyong National University)
Lee, Soo-Jin (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Ahn, Jihye (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Lee, Eunjeong (Korea Environmental Science and Technology Institute)
Joh, Seongeok (Korea Environmental Science and Technology Institute)
Lee, Yang-Won (Department of Spatial Information Engineering, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.1, 2020 , pp. 41-53 More about this Journal
Abstract
The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.
Keywords
Offshore; Fish catch; Sea state; Artificial intelligence; Deep learning;
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Times Cited By KSCI : 6  (Citation Analysis)
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