• Asian Journal of Turfgrass Science
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• v.26 no.2
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• pp.123-128
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• 2012

Kentucky bluegrass (Poa pratensis) is most widely used in golf courses and athletic fields. Weakness of Kentucky bluegrass is shallow root zone and has weak tolerance to shade. One of the biggest disadvantages is high demand of water. Water content is important factor to maintain excellent color and quality of turfgrass. There are two irrigation methods which are 'deep and infrequent (DI)' and 'Light and frequent (LI)'. The objective of the study is to investigate Kentucky bluegrass growth treated by different irrigation frequency. Three irrigation frequency were made; no irrigation, every other day, and weekly. The same amount of water was used between every other day and weekly irrigation except no irrigation. No irrigation mean no artificial water supply and precipitation only. No irrigation treatment produced turfgrass quality lower than acceptable rating of six in July and August. Under the weather condition of 2011, no irrigation could not maintained acceptable turfgrass quality. No significant differences were found for Kentucky bluegrass quality between DI and LI.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.643-650
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• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.233-239
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• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Research in Plant Disease
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• v.7 no.2
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• pp.102-106
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• 2001

Currently the rice false smut caused by Ustilaginoidea virens (Cooke) Takah. has occurred widely in Korea. A survey on the disease incidence at rice fields in 8 inland provinces was carried out in 2000, showing that the disease occurred at 104 (7.5%) out of 1,152 rice fields examined, ranging from 1.5% to 13.7% in provincial average. It was found that the disease incidence was greatly affected by local weather conditions and rice cultivars. In case of the most susceptible cultivar Namchunbyeo, the disease incidence was only about 1.3% in Icheon, while it was over 20% in Namwon which had weather conditions of the shorter sunshine period (about 64%) and the higher amount of precipitation (about 130%) during the rice cultivation than Icheon. In Icheon, artificial inoculation of the pathogen failed to induce the disease, probably because of the weather conditions unfavorable to the disease development, which also suggests that its incidence may be dependent on the weather conditions. Susceptibility of rice cultivars to the disease varied greatly; eight resistant cultivars including Heukjinjubyeo were not damaged by the disease, but 2 cultivars including Geumnambyeo and Namchunbyeo were severely damaged, having more than 20% of the disease incidence. Among chemicals tested fur the control of the rice false smut tebuconazole WP showed the highest control efficacy of 83∼88% on cvs. Geumnambyeo and Namchunbyeo. Other chemicals such as azoxystrobin WP and ferimzone WP also effectively suppressed the disease development in the field trials.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.74-82
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• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.4
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• pp.1-13
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• 2017

The purpose of this study is to analyze a term of decade of water quality data of the Mulgum intake station on the Nakdong river(dam) to find the cause of algal blooms and to set an alternative to prevent artificial lake water pollution. Our study shows that water quality changes have regular periodic regularity and there was a certain correlation between specific analytical items. According to the analysis results of each factor, the decline in precipitation was not the main reason for algal blooms. TP concentration had a slight effect on Chl-a concentration but was not a limiting nutrient of a bloom. TN concentration had a strong correlation with Chl-a and strongly negative correlation with temperature, but was not a bloom's limiting nutrient, and was only a dependent variable. As the temperature was negatively correlated with the Chl-a concentration, it is found that the aspect of the ecological influence of the temperature was the most important factor of the phytoplankton concentration change. The N/P ratio lies under a power function with a high degree of reliability by the TP concentration, and the phenomenon appeared to be the same as the results of two other comparative areas. This result confirms that TN is dependent on TP and the biota in the lake that TN is a dependent variable whose concentration is determined by TP it. In conclusion, the increase in lake bloom is the result of a food chain change, and it is necessary to control the ecosystem by the food chain in the lake in order to reduce the lake's bloom. In particular, it is important to keep the benthic ecosystem as wide as possible in the aerobic state.

• Journal of the Korean Geographical Society
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• v.48 no.5
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• pp.615-628
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• 2013

The waterfall can be formed by difference between the height of up and down part in new channel, is formed by channel avulsion that rapidly changing of river channel course. This study described types and processes of waterfalls by channel avulsion, and analyzed rates and factors of waterfall recession, on object to 7 waterfalls in South Korea. Bulyeong falls at Uljin-gun, Yongchu falls at Yeongdeok-gun, Jikyeon falls at Yanggu-gun and Gwangpum falls at Uljin-gun are formed by natural incised meander cutoff. Samhyeongje falls at Taebaek-si and Guryong falls are formed by river capture processes, and Palbong falls at Chungju-si is formed by artificial channel cutting for farm land secured. The locations of waterfalls gradually moved to upstream over time by head erosion. The recession rates were measured by 3~4m/ka on Bulyeong falls, Yongchu falls, Jikyeon falls and Samhyeongje falls, to estimate of formation age. Recession rates of these 4 waterfalls were analyzed that have clearly positive correlations with drainage area, precipitation, corrosion and weathering capability of bedrock, and initial height of waterfall.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.170-178
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• 2012

In this study, in order to provide an information for artificial cultivation of wild mushroom, the meteorological effects on wild mushroom appearance were examined using daily meteorological observations in Chiak National Park. The survey of wild mushroom appearance was carried out once a month from June to October. Under high temperature and humidity conditions in July and August, the appearance of wild mushroom was frequent. In contrast, lower number of wild mushroom appeared in October. Wild mushroom appearance was affected by solar radiation, relative humidity, precipitation, and soil water content whereas the impact of air and soil temperature was lower than that of other meteorological elements.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.383-393
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• 2018

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by $7{\times}6$ array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.