• Korean Journal of Soil Science and Fertilizer
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• v.25 no.3
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• pp.231-241
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• 1992

To study the relationships between major micrometeorological elements and their influences on evapotranspiration(ET) in the canopy of two rice cultivars, Daecheongbyo and Samgangbyo, synoptic meteorological factors, micrometeorological elements and ET from the canopy and biomass production were observed at various growth stages in the paddy field of Suwon Weather Forcast Office in 1989. ET from the rice community was highly correlated with the following factors in order of pan evaporation>air temperature>leaf temperature>solar radiation>sunshine duration>difference in vapor pressure depicit(VPD)>water temperature. ET observed showed higher correlation with the evaporation from small pan than that from Class A pan. Varietal difference would be noted in the relationships between ET in Samgangbyo canopy and the evaporations observed from the pans, with which closer a correlation was found in Samgangbyo than in Daecheongbyo. The ratio of canopy ET to the evaporation from Class A pan was maintained over 1.0 through the growth stages with the maximum of 1.9 at the late August. The evaporation observed from Class A pan was amounted to 71.9% of that from small pan. ET was better correlated with solar radiation than with net radiation which reached about 66% of solar radiation. Maximum temperature showed higher correlation with ET than mean air temperature, and also wind speed of 1m above ground revealed positive correlation. The relative humidity, however, had no correlation with the exception of ET in rainy days. A regression model developed to estimate ET as a function of meteorological elements being described with $R^2$ of 0.607 as : $ET=-5.3594+0.7005Pan\;A+0.1926T_{mean}+0.0878_{sol}+0.025RH$.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.168-187
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• 2006

Windy meteorological conditions and dried fire fuels due to higher atmospheric instability and dryness in the lower troposphere can exacerbate fire controls and result in more losses of forest resources and residential properties due to enhanced large wildland fires. Long-term (1979-2005) climatology of the Haines Index reconstructed in this study reveals that spatial patterns and intra-annual variability of the atmospheric instability and dryness in the lower troposphere affect the frequency of wildland fire incidences over the Korean Peninsula. Exponential regression models verify that daily high Haines Index and its monthly frequency has statistically significant correlations with the frequency of the wildland fire occurrences during the fire season (December-April) in South Korea. According to the climatic maps of the Haines Index created by the Geographic Information System (GIS) using the Digital Elevation Model (DEM), the lowlands below 500m from the mean sea level in the northwestern regions of the Korean Peninsula demonstrates the high frequency of the Haines Index equal to or greater than five in April and May. The annual frequency of the high Haines Index represents an increasing trend across the Korean Peninsula since the mid-1990s, particularly in Gyeongsangbuk-do and along the eastern coastal areas. The composite of synoptic weather maps at 500hPa for extreme events, in which the high Haines Index lasted for several days consecutively, illustrates that the cold low pressure system developed around the Sea of Okhotsk in the extreme event period enhances the pressure gradient and westerly wind speed over the Korean Peninsula. These results demonstrate the need for further consideration of the spatial-temporal characteristics of vertical atmospheric components, such as atmospheric instability and dryness, in the current Korean fire prediction system.

• Journal of Korean Society of Forest Science
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• v.87 no.4
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• pp.549-556
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• 1998

To develop the effective methods of flowering stimulation, Hinoki cypress (Chamaecyparis obtusa Sieh. et Zuec.) grafts growing in a seed orchard and in a clone bank in Southern Breeding Station of Cheju were applied with gibberellin treatments, and predicted the seed production potential. In the seed orchard, $GA_{4/7}$ 1.5cc was injected into the stem of drafts and sprayed whale tree crown with $GA_3$ 300ppm and $GA_{4/7}$ 300ppm. Un the other hand, in the clonal archives, drafts were given intrusion of $GA_{4/7}$ 1.5cc into the excised and open inner part of bark wind $GA_3$ 20mg and sprayed with $GA_3$ 300ppm. Additionally, grafts growing in the seed orchard were treated with gibberellins at 3 different periods of time and 3 different treatments during the growing season. The results obtained here are summarized as follows : 1. All of the applications of Gilbberellin promoted female flower formation. Among these, the treatment of intrusion of $GA_{4/7}$ 1.5cc into the excised and open inner part of bark was racist effective, followed by the spraying of $GA_3$ 300ppm. Similarly, the applications of gibberellin promoted male flower formation. 2. Regarding the time of applications, treatment on August 15 was more effective than those of August 31 and September 11 in the stimulation of female flowers. On the contrary, there was no significant difference in the number of male flowers among 3 different time treatments. 3. It was supposed that the application of the intrusion of $GA_{4/7}$ 1.5cc into the excised and open inner part of bark on August 15 showing the best effect in female flower formation can produce 22.12kg seeds per ha. 4. Considerable significant difference existed among clones for both female and male flower formations. 5. Flower formation, especially female flower formation, seemed to be partially associated with the genetic potential of individual trees.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.