• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.279-285
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• 2019

To estimate daily canopy photosynthesis, accurate estimation of canopy light interception according to a daily solar position is needed. However, this process needs a lot of cost, time, manpower, and difficulty when measuring manually. Various modeling approaches have been applied so far, but it was difficult to accurately estimate light interception by conventional methods. The objective of this study is to estimate the spatial distributions of light interception and photosynthetic rate of paprika with time by using 3D-scanned plant models and optical simulation. Structural models of greenhouse paprika were constructed with a portable 3D scanner. To investigate the change in canopy light interception by surrounding plants, the 3D paprika models were arranged at $1{\times}1$ and $9{\times}9$ isotropic forms with a distance of 60 cm between plants. The light interception was obtained by optical simulation, and the photosynthetic rate was calculated by a rectangular hyperbola model. The spatial distributions of canopy light interception of the 3D paprika model showed different patterns with solar altitude at 9:00, 12:00, and 15:00. The total canopy light interception decreased with an increase of surrounding plants like an arrangement of $9{\times}9$, and the decreasing rate was lowest at 12:00. The canopy photosynthetic rate showed a similar tendency with the canopy light interception, but its decreasing rate was lower than that of the light interception due to the saturation of photosynthetic rate of upper leaves of the plants. In this study, by using the 3D-scanned plant model and optical simulation, it was possible to analyze the light interception and photosynthesis of plant canopy under various conditions, and it can be an effective way to estimate accurate light interception and photosynthesis of plants.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.