• Journal of Environmental Health Sciences
• /
• v.28 no.3
• /
• pp.1-8
• /
• 2002

The experiments for the characterization of inactivation were performed in a series of batch processes with the total coliform as a general indicator organism based on chlorine, chlorine dioxide and ozone as disinfectants. The water sam-ples were taken from the outlet of settling basin in a conventional surface water treatment system that is provided with the raw water drawn from the mid-stream of the Han River. The inactivation of total coliform was experimentally ana-lyzed for the dose of disinfectant contact time, pH, Temperature and DOC. The nearly 2.4,3.0,3.9 log inactivation of total coliform killed by injecting 1 mか1 at 5 minutes for chlorine, chlorine dioxide and ozone. For the inactivation of 99.9%(3 log), Disinfectants required were 1.70, 1.00 and 0.60 mか1 for chlorine, chlorine dioxide and ozone, respec-tively. The higher the pH is, the poorer the disinfections effects are in the range of pH 6-9 by using chlorine and ozone. But the irfluence of pH value on killing effects of chlorine dioxide is weak. The parameters estimated by the models of Chick-Watson, Hom, and Selleck from our experimental data obtained for chlorine are: log(N/ $N_{0}$ )=-0.16 CT with n= 1, log(N/ $N_{0}$ )=-0.71 $C^{0.87}$T with n$\neq$1, for Chicks-Watson model, log (N/ $N_{0}$ )= -1.87 $C^{0.47}$ $T^{0.36}$ for Hom model. For chlorine dioxide are: log(N/ $N_{0}$ )= -1.53 CT with n = 1, log(N/ $N_{0}$ )= -2.29 $C^{0.94}$T with n$\neq$1,, for Chicks-Watson model, log(N/ $N_{0}$ )= -3.64 $C^{0.43}$ $T^{0.24}$ for Hom model and for ozone are: log(N/ $N_{0}$ )= -2.59 CT with n = 1, log(N/ $N_{0}$ )= -2.28 $C^{0.36}$T with n$\neq$1, for Chicks-Watson model, log(N/ $N_{0}$ )= -4.53 $C^{0.26}$ $T^{0.19}$ for Hom model.19/ for Hom model.

• Journal of Environmental Health Sciences
• /
• v.43 no.6
• /
• pp.478-490
• /
• 2017

Objectives: The association of air pollution levels and land-use types with changes in the prevalence of allergic diseases (allergic conjunctivitis, allergic rhinitis, asthma, and atopic dermatitis) was investigated for seven metropolitan cities in Korea Methods: Data on daily hospital visits and admissions (of those under 19 years old) for 2003-2012 were obtained from the National Health Insurance Cooperation. Meteorological data on daily mean temperature, humidity, and air pressure were obtained from the Korea Meteorological Administration. Daily mean or maximum concentration data for five pollutants ($PM_{10}$, $O_3$, $NO_2$, $SO_2$, and CO) as measured at air quality monitoring sites operated by the Ministry of Environment were used. We estimated excess risk and 95% confidence intervals for the increasing interquatile range (IQR) of each air pollutant using Generalized Additive Models (GAM) appropriate for time series analysis. Results: In this study, we observed a significant association between the IQR increases of air pollutants and the prevalence risk of allergic diseases (allergic conjunctivitis, allergic rhinitis, asthma, and atopic dermatitis) in all metropolitan cities after adjusting for temperature, humidity, and air pressure at sea level. Among the air pollutants, $NO_2$ and $PM_{10}$ were associated with the prevalence of asthma, and $O_3$ was associated with only allergic conjunctivitis in regression analysis. However, in GAM analysis considering land-use, $O_3$ and $SO_2$ were associated with allergic conjunctivitis, PM10, O3, NO2, and CO were associated with allergic rhinitis, and $PM_{10}$, $O_3$ and $NO_2$ were associated with asthma in industrial area. Conclusion: This study found a significant association between air pollution and the prevalence of allergic related diseases in industrial areas. More detailed research considering mixed traffic-related air pollution (TRAP) and conducting meta-analyses combining data of the all cities is required.

• The Korean Journal of Financial Management
• /
• v.21 no.1
• /
• pp.149-181
• /
• 2004

In this study we perform empirical tests using KOSPI return to investigate the existence of nonlinear characteristics in the generating process of stock returns. There are three categories in empirical tests; the test of nonlinear dependence, nonlinear stochastic process and nonlinear deterministic chaos. According to the analysis of nonlinearity, stock returns are not normally distributed but leptokurtic, and appear to have nonlinear dependence. And it's decided that the nonlinear structure of stock returns can not be completely explained using nonlinear stochastic models of ARCH-type. Nonlinear deterministic chaos system is the feedback system, which the past incidents influence the present, and it is the fractal structure with self-similarity and has the sensitive dependence on initial conditions. To summarize the results of chaos analysis for KOSPI return, it is the persistent time series, which is not IID and has long memory, takes biased random walk, and is estimated to be fractal distribution. Also correlation dimension, as the approximation of fractal dimension, converged stably within 3 and 4, and maximum Lyapunov exponent has positive value. This suggests that chaotic attractor and the sensitive dependence on initial conditions exist in stock returns. These results fit into the characteristics of chaos system. Therefore it's decided that the generating process of stock returns has nonlinear deterministic structure and follow chaotic process.

• The Korean Journal of Applied Statistics
• /
• v.32 no.6
• /
• pp.795-835
• /
• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of Korea Water Resources Association
• /
• v.37 no.1
• /
• pp.77-86
• /
• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.9 no.4
• /
• pp.217-227
• /
• 2007

The western cherry fruit fly, Rhagoletis indifferens Curran (Diptera:Tephritidae), is the most important pest of cultivated cherries in the Pacific Northwest area of the United States, being widely distributed throughout Oregon, Washington, Montana, Utah, Idaho, Colorado and parts of Nevada. The control of R. indifferens has been based on calendar sprays after its first emergence because of their zero tolerance for quarantine. Therefore, a good prediction model is needed for the spray timing. This study was conducted to obtain the empirical population dynamic information of R. indifferens after overwintering in the major cherry growing area of the Pacific Northwest of the United States, where the information is critically needed to develop and validate the prediction model of the fruit fly. Adult fly populations were monitored by using yellow sticky and emergence traps. Larvae growth and density in fruits were observed by fruit sampling and the pupal growth and density were monitored by pupal collection traps. The first adult was emerged around mid May and a large number of adults were caught in early June. A fruit had more than one larva from mid June to early July. A large number of pupae were caught in early July. The pupae were collected in various period of time to determine the effect of pupation timing and the soil moisture content during the winter. A series of population density data collected in each of the developmental stage were analyzed and organized to provide more reliable validation information for the population dynamic models.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.2
• /
• pp.104-115
• /
• 2013

In a shallow water waveguide, reverberation signals and their Doppler effects form the primary limitation on sonar system performance. Therefore, in the reverberation-limited environment, it is necessary to estimate the reverberation level to be encountered under the conditions in which the sonar system is operated. In this paper, high-frequency reverberation model capable of simulating the reverberation signals received by a high-speed moving source in a range independent waveguide is suggested. In this model, eigenray information from the source to each boundary is calculated using the ray-based approach and the optimizing method for the launch angles. And the source receiving position changed by the moving source is found by a scattering path-finding algorithm, which considers the speed and direction of source and sound speed to find the path of source movement. The scattering effects from sea surface and bottom boundaries are considered by APL-UW scattering models. The model suggested in this paper is verified by a comparison to the measurements made in August 2010. Lastly, this model reflects well statistical properties of the reverberation signals.

• Journal of Distribution Science
• /
• v.12 no.6
• /
• pp.49-57
• /
• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.

• Journal of Korea Water Resources Association
• /
• v.47 no.9
• /
• pp.825-838
• /
• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• Journal of Korea Water Resources Association
• /
• v.41 no.3
• /
• pp.341-351
• /
• 2008

Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.