• Korean Journal of Ecology and Environment
• /
• v.44 no.3
• /
• pp.264-271
• /
• 2011

At the Jangheung multipurpose dam, which is on the Tamjin River, a trapping and trucking operation was established to maintain continuous upstream migration of fish,. To facilitate fish gathering, installation of an effective fishing trap was required. In this study, we evaluated the fish trap, established at the Jangheung dam, using PIT (Passive Integrated Transponder) telemetry. A total of 254 individuals from 15 species were monitored. Among these tagged species, 36 individuals from 6 species (Carassius auratus, C. cuvieri, Zacco temminckii, Z. platypus, Pungtungia herzi, and Pseudobagrus koreanus) were detected; a 14.2% detection rate. C. auratus recorded the highest detection rate of 44.2% while P. herzi was 14.3%. Z. temminckii and Z. platypus showed relatively low detection, 5% and 7.7% respectively. Some of individuals from C. auratus and Z. platypus did not pass through the antenna at the first attempt but were continuously detected on multiple days. There were no statistical differences in body size (total length, standard length and body weight) of individuals that did or did not swim into the trap (Mann-Whitney U test, p>0.05). Fish mainly swam into the trap during outflow of water from the dam (Mann-Whitney U test, p<0.001) and showed a higher detection frequency in daytime than nighttime (Mann-Whitney U test, p<0.001). Thus, for fish movement into the trap, external factors such as outflow from dam and time of day have important roles. Based on detection rate, not all fishes showed upstream migration but represented selective migration. Consequently, the establishment of flexible outflow strategies that take into consideration ecological characteristics of fishes should required for improving the efficiency of fishway.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.603-614
• /
• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.