• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Advances in environmental research
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• v.4 no.4
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.228-233
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• 2007

Pear psylla, Caccopsylla pyricola (Homoptera: Psyllidae), is a serious insect pest in pear orchards. C. pyricola overwinters as adults under rough bark scales of pear trees. When the weather warms up in the spring, the overwintered adults become active, climb up to the tree branches, and inhabit on fruit twigs to lay eggs. This study was conducted to develop a forecasting model for the onset of upward-movement of overwintered C. pyricola adults to control them by timely spraying of petroleum oil. The adult population densities were observed under rough barks (B) and on fruit twigs (T) of pear trees. Relative upward-movement rates (R) were calculated as T/(B+T). Low threshold temperatures for the activation of overwintered C. pyricola adults were selected arbitrarily from 5 to $9^{\circ}C$ at a $1^{\circ}C$ interval. Then, the days (D) when daily maximum air temperatures were above each low threshold temperature were counted from 1 February until to the dates with R $\geq$ 0.8. The same methods were applied for the prediction of the first observation of eggs. The variation of coefficients (CV) for the mean Des were lowest with the low threshold temperature of $6^{\circ}C$. At this selected threshold temperature, the upward movement of C. pyricola adults occurred with 12 D and they started laying eggs with 25 D. In the field validation, the model outputs with the $6^{\circ}C$ threshold temperature reasonably well explained the observed data in Suwon and Cheonan in 2002. Practical usages of the model were also discussed.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1117-1125
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• 2012

The occurrence of food poisoning is usually modeled by meteorological variables like the temperature and the humidity. In this paper, we investigate the relationship between food poisoning occurrence and climate variables in Korea and compare Poisson regression and autoregressive moving average model to select the forecast model. We confirm that lagged climate variables affect the food poisoning occurrences. However, it turns out that, from the viewpoint of the prediction, the number of previous occurrences is more influential to the current occurrence than meteorological variables and Poisson regression model is less reliable.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.1
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• pp.109-118
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• 1997

In this study, we examined the structural analysis of water demand fluctuation for water distribution control of water supply network. In order to analyze for the length of stationary time series, we calculate autocorrelation coefficient of each case equally divided data size. As a result, it was found that, with the data size of around three months, any case could be used as stationary time series. we analyze cross-correlation coefficient between the daily water consumption's data and primary influence factors. As a result, we have decided to use weather conditions and maximum temperature as natural primary factors and holidays as a social factor. Applying the multiple ARIMA model, we obtains an effective model to describe the daily water demand prediction. From the forecasting result, even though we forecast water distribution quantity of the following year, estimated values well express the flctuations of measurements. Thus, the suitability of the model for practical use can be confirmed. When this model is used for practical water distribution control, water distribution quantity for the following day should be found by inputting maximum temperature and weather conditions obtained from weather forecast, and water purification plants and service reservoirs should be operated based on this information while operation of pumps and valves should be set up. Consequently, we will be able to devise a rational water management system.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.625-633
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• 2015

The effect of altitude on thermal conduction, surface temperature, and thermal radiation of partial arc was investigated on the basis of molecular gas dynamics to facilitate a deep understanding of the pollution surface discharge mechanism. The DC flashover model was consequently modified at high altitude. The validity of the modified DC flashover model proposed in this paper was proven through a comparison with the results of high-altitude simulation experiments and earlier models. Moreover, the modified model was found to be better than the earlier modified models in terms of forecasting the flashover voltage. Findings indicated that both the thermal conduction coefficient and the surface thermodynamics temperature of partial arc had a linear decrease tendency with the altitude increasing from 0 m to 3000 m, both of which dropped by approximately 30% and 3.6%, respectively. Meanwhile, the heat conduction and the heat radiation of partial arc both had a similar linear decrease of approximately 15%. The maximum error of DC pollution flashover voltage between the calculation value according to the modified model and the experimental value was within 6.6%, and the pollution flashover voltage exhibited a parabola downtrend with increasing of pollution.

• International Journal of Highway Engineering
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• v.17 no.5
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• pp.1-10
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• 2015

PURPOSES : A finite difference model considering snow melting process on porous asphalt pavement was derived on the basis of heat transfer and mass transfer theories. The derived model can be applied to predict the region where black-ice develops, as well as to predict temperature profile of pavement systems where a de-icing system is installed. In addition, the model can be used to determined the minimum energy required to melt the ice formed on the pavement. METHODS : The snow on the porous asphalt pavement, whose porosity must be considered in thermal analysis, is divided into several layers such as dry snow layer, saturated snow layer, water+pavement surface, pavement surface, and sublayer. The mass balance and heat balance equations are derived to describe conductive, convective, radiative, and latent transfer of heat and mass in each layer. The finite differential method is used to implement the derived equations, boundary conditions, and the testing method to determine the thermal properties are suggested for each layer. RESULTS: The finite differential equations that describe the icing and deicing on pavements are derived, and we have presented them in our work. The framework to develop a temperature-forecasting model is successfully created. CONCLUSIONS : We conclude by successfully creating framework for the finite difference model based on the heat and mass transfer theories. To complete implementation, laboratory tests required to be performed.