• Korean Journal of Environmental Biology
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• v.27 no.1
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• pp.6-19
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• 2009

We investigated the macrozoobenthos at major two harbors of Korea in July and August 2007 in order to check the changes in the species composition due to the invasive species and to make a species inventory at each harbor system. At the Incheon Harbor, a total of 88 species was sampled with abundance of 3,212 ind. m$^{-2}$ and biomass of 239 g m$^{-2}$. The most dominant species was Tharyx sp. belong to polychaete taxa, followed by Chaetozone setosa in the harbor area. The dominant species of outer area were Musculus senhousia and Sternaspis scutata. The diversity index ranged between 0.9$\sim$2.4, and evenness index between 0.3$\sim$0.9, and richness index between 1.8$\sim$3.9. Benthic pollution index ranged between 16$\sim$74. The highest benthic pollution index was at station 4. On the other hand the lowest value was at station 6, where a large amount of M. senhousia belong to mollusca occurred. At the Busan Harbor, a total of 89 species was sampled with density of 1,845 ind. m$^{-2}$ and biomass of 133.6 g m$^{-2}$ in August 2007. The most dominant species was Tharyx sp., followed by M. japonica and Cirratulus cirrata within harbor area. M. japonica was dominant species in the outer area. The diversity index ranged between 0.7$\sim$2.2, evenness index between 0.3$\sim$1.0, and richness index between 1.1$\sim$4.1. Benthic pollution index ranged between 31$\sim$90. The lowest benthic pollution index was found at site 2 within harbor area.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.