• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.209-221
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• 2006

The purpose of this study is to analyze the effects of climate change on the nonpoint source pollution in a small watershed using a mid-range model. The study area is a basin in a rural area that covers 384 ha with a composition of 50% forest and 19% paddy. The hydrologic and water quality data were monitored from 1996 to 2004, and the feasibility of the GWLF (Generalized Watershed Loading function) model was examined in the agricultural small watershed using the data obtained from the study area. As one of the studies on climate change, KEI (Korea Environment Institute) has presented the monthly variation ratio of rainfall in Korea based on the climate change scenario for rainfall and temperature. These values and observed daily rainfall data of forty-one years from 1964 to 2004 in Suwon were used to generate daily weather data using the stochastic weather generator model (WGEN). Stream runoff was calibrated by the data of $1996{\sim}1999$ and was verified in $2002{\sim}2004$. The results were determination coeff, ($R^2$) of $0.70{\sim}0.91$ and root mean square error (RMSE) of $2.11{\sim}5.71$. Water quality simulation for SS, TN and TP showed $R^2$ values of 0.58, 0.47 and 0.62, respectively, The results for the impact of climate change on nonpoint source pollution show that if the factors of watershed are maintained as in the present circumstances, pollutant TN loads and TP would be expected to increase remarkably for the rainy season in the next fifty years.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.1
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• pp.70-77
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• 2016

The object of this study was to evaluate the effects of climatic elements on potato yield and create a model for estimating the potato yield. We used 35 yield data of Sumi variety produced in mulching cultivation from 17 regions over 11 years. According to the results, some climatic elements showed significant level of correlation coefficient with marketable yield of potato. Totally 22 items of climatic elements appeared to be significant. Especially precipitation for 20 days after planting (Prec_1 & 2), relative humidity during 11~20 days after planting (RH_2), precipitation for 20 days before harvest (Prec_9 & 10), sunshine hours during 50~41 days before harvest (SH_6) and 20 days before harvest (SH_9 & 10), and days of rain during 10 days before harvest (DR_10) were highly significant in quadratic regression analysis. 22 items of predicted yield ($Y_i=aX_i{^2}+bX_i+c$) were induced from the 22 items of climatic elements (step 1). The correlations between the predicted yields and marketable yield were stepwised using SPSS, statistical program, and we selected a model (step 2), in which 4 items of independent variables ($Y_i$) were used. Subsequently the $Y_i$ were replaced with the equation in step 1, $aX_i{^2}+bX_i+c$. Finally we derived the model to predict the marketable yield of potato as below. $$Y=-336{\times}DR_-10^2+854{\times}DR_-10-0.422{\times}Prec_-9^2+43.3{\times}Prec_-9\\-0.0414{\times}RH_-2^2+46.2{\times}RH_-2-0.0102{\times}Prec_-2^2-7.00{\times}Prec_-2-10039$$.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.316-322
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• 2009

This paper presents a preliminary study to monitor the lateral behavior of pile foundation using multiplexed fiber Bragg grating(FBG) sensors. In the Preliminary study, an 1.7 meter long cantilever beam with the shape of square hollow box was fabricated and tested under the static loading. Four FBG sensors were multiplexed in a single optical fiber and installed into the top and bottom of the cantilever beam. The strains are directly measured from FBG sensors followed by curvature calculations based on the plane section assumption. Vertical deflections are then estimated using the regression analyses based on the geometric relationships. It has been found that excellent correlation with conventional sensing system was observed. The success of the test encourages the use of the FBG sensing system as a monitoring system for pile foundations. However, further consideration should be given in the case of the sensor malfunction for the practical purpose.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.337-346
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• 2010

Climate change has a huge impact on various parts of the world. This study quantified and analyzed the effects on hydrological behavior caused by climate, vegetation canopy and land use change of Soyanggang dam watershed (2,694.4 $km^2$) using the semi-distributed model SWAT (Soil Water Assessment Tool). For the 1997-2006 daily dam inflow data, the model was calibrated with the Nash-Sutcliffe model efficiencies between the range of 0.45 and 0.91. For the future climate change projection, three GCMs of MIROC3.2hires, ECHAM5-OM, and HadCM3 were used. The A2, A1B and B1 emission scenarios of IPCC (Intergovernmental Panel on Climate Change) were adopted. The data was corrected for each bias and downscaled by Change Factor (CF) method using 30 years (1977-2006, baseline period) weather data and 20C3M (20th Century Climate Coupled Model). Three periods of data; 2010-2039 (2020s), 2040-2069 (2050s), 2070-2099 (2080s) were prepared for future evaluation. The future annual temperature and precipitation were predicted to change from +2.0 to $+6.3^{\circ}C$ and from -20.4 to 32.3% respectively. Seasonal temperature change increased in all scenarios except for winter period of HadCM3. The precipitation of winter and spring increased while it decreased for summer and fall for all GCMs. Future land use and vegetation canopy condition were predicted by CA-Markov technique and MODIS LAI versus temperature regression respectively. The future hydrological evaluation showed that the annual evapotranspiration increases up to 30.1%, and the groundwater recharge and soil moisture decreases up to 55.4% and 32.4% respectively compared to 2000 condition. Dam inflow was predicted to change from -38.6 to 29.5%. For all scenarios, the fall dam inflow, soil moisture and groundwater recharge were predicted to decrease. The seasonal vapotranspiration was predicted to increase up to 64.2% for all seasons except for HadCM3 winter.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.121-133
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• 2015

The TANK model has been widely used in rainfall-runoff modeling due to its simplicity of concept and computation while achieving forecast accuracy. A major barrier to the model application is to determine parameter values for ungauged watersheds, leading to the need of a method for the parameter estimation. The objective of this study was to develop regression equations for estimating the 3th TANK model parameters considering their variations for the ungauged watersheds. Thirty watersheds of dam sites and stream stations were selected for this study. A genetic algorithm was used to optimize TANK model parameters. Watershed characteristics used in this study include land use percent, watershed area, watershed length, and watershed average slope. Generalized equations were derived by correlating to the optimized parameters and the watershed characteristics. The results showed that the TANK model, with the parameters determined by the developed regression equations, performed reasonably with 0.60 to 0.85 of Nash-Sutcliffe efficiency for daily runoff. The developed regression equations for the TANK model can be applied for the runoff simulation particularly for the ungauged watersheds, which is common for upstream of agricultural reservoirs in Korea.

• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.181-191
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• 2014

Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.37-45
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• 2015

Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Therefore regression models (or rating curves) are often used to interpolate water quality data. LOADEST has nine regression models to estimate water quality data, and one regression model needs to be selected automatically or manually. The nine regression models in LOADEST and auto-selection by LOADEST were evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry of Environment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) and coefficient of determination ($R_2$) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST provided higher NSE and $R_2$, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. In addition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.203-212
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• 2002

This study was conducted to remove the urban heat island effects embedded in the interpolated surfaces of daily minimum temperature in the Korean Peninsula. Fifty six standard weather stations are usually used to generate the gridded temperature surface in South Korea. Since most of the weather stations are located in heavily populated and urbanized areas, the observed minimum temperature data are contaminated with the so-called urban heat island effect. Without an appropriate correction, temperature estimates over rural area or forests might deviate significantly from the actual values. We simulated the spatial pattern of population distribution within any single population reporting district (city or country) by allocating the reported population to the "urban" pixels of a land cover map with a 30 by 30 m spacing. By using this "digital population model" (DPM), we can simulate the horizontal diffusion of urban effect, which is not possible with the spatially discontinuous nature of the population statistics fer each city or county. The temperature estimation error from the existing interpolation scheme, which considers both the distance and the altitude effects, was regressed to the DPMs smoothed at 5 different scales, i.e., the radial extent of 0.5, 1.5, 2.5, 3.5 and 5.0 km. Optimum regression models were used in conjunction with the distance-altitude interpolation to predict monthly normals of daily minimum temperature in South Korea far 1971-2000 period. Cross validation showed around 50% reduction in terms of RMSE and MAE over all months compared with those by the conventional method.conventional method.