• Journal of Environmental Science International
• /
• v.25 no.4
• /
• pp.603-613
• /
• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• International Journal of Highway Engineering
• /
• v.12 no.2
• /
• pp.1-7
• /
• 2010

This study deals with the unsignalized intersection delay in the urban transportation demand forecasting. The objectives are to develop the unsignalized intersection delay models and to comparatively analyze the applicability of the above models. In pursuing the above, this study gives particular attentions to simulating by KHCS program and implementing the case study of Cheongju using EMME/2. The major findings are the followings. First, the 8 unsignalized intersection delay models were developed through 480 simulating results, which are all statistically significant. Second, the estimates by the unsignalized delay models were analyzed to be most fitted to the observed traffic volume data.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.3
• /
• pp.154-161
• /
• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Journal of Wetlands Research
• /
• v.11 no.2
• /
• pp.107-121
• /
• 2009

Many rainfall-runoff models have been used for the flood forecasting. However, the determination of rainfall-runoff model parameters is very difficult. In this study, we investigated the efficiency of flood forecasting models by studying the optimization techniques for parameter calibration of SFM, Tank, and SSARR models. We analyzed the correlations between parameters in optimization techniques, then classified the parameters into parameter groups. For this we applied the sequential calibration method through the sensitivity analysis. As the results of the analysis, the parameter groups clibration method showed better result for peak flow and clibtation time.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3923-3942
• /
• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Korean System Dynamics Review
• /
• v.5 no.2
• /
• pp.67-88
• /
• 2004

This paper attempts to explore the generic pitfalls of the traditional number-crunching methods adopted thus far for the forecast of newly emerging market trends, and present an alternative by introducingsystems thinking to the portable Internet service market as an example, followed by its rationale as a new tool for forecasting and some reasoning about why traditional methods are no longer appropriate. Most adoption models in general to forecast market trends have several limitations due to theirbasic assumptions and prospective. First, they fail to capture dynamic interactions among the factors involved over time, with implicit assumptions of 'unilateral causality' in that each factor contributes as a cause to the effect, i.e., causality runs one way; each factor acts independently the weighting factor of each is fixed, etc. Second, the number-crunching models have no way of taking into account the impact of delayed feedback often caused by introducing new policies and legislative changes on the whole system under investigation. Third, there is not a way to reflect the effect of competition by players.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.402-405
• /
• 1991

This paper represents a new method for computing reliability indices by using Large Deviation method which is one of the probabilistic production cost simulations. The reliability measures are based on the models used for the loads and for the generating unit failure states. In computing these measures it has been tacitly assumed that the values of all parameters in the models are precisely known. In fact, however, some of these values must often be chosen with a considerable degree of uncertainty involved. This is particularly true for the forecast peak loads in the load model, where there is an inherent uncertainty in the method of forecasting, which are frequently based on insufficient statistics. In this paper, the effect of load forecasting uncertainty on the LOLP(Loss of Load Probability), is investigated. By applying the Large Deviation method to the IEEE Rilability Test System, it is verified that the proposed method is generally very accurate and very fast for computing system reliability indices.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.12 no.1
• /
• pp.115-122
• /
• 1992

Stochastic rainfall-runoff process model which is mainly used in real-time streamflow forecasting is Transfer Function(TF) model that has a simple structure and can be easy to formulate state-space model. However, in order to forecast the streamflow accurately in real-time using the TF model, it is not only necessary to determine accurate structure of the model but also required to reduce forecasting error in early stage. In this study, after introducing 5-day Antecedent Precipitation Index (API5), which represents the initial soil moisture condition of the watershed, by using the threshold concept, the TF models in each API5 are identified by Box-Jenkins method and the results are compared with each other.

• Journal of Information Technology Applications and Management
• /
• v.21 no.1
• /
• pp.177-184
• /
• 2014

This research analyzes the effects of factors on the demands for outbound to the countries such as Japan, China, the United States of America, Thailand, Philippines, Hong Kong, Singapore and Australia, the countries preferred by many Koreans. The factors for this research are (1) economic variables such as Korea Composite Stock Price Index (KOSPI), which could have influences on outbound tourism and exchange rate and (2) unpredictable events such as diseases, financial crisis and terrors. Regression analysis was used to identify relationship based on the monthly data from January 2001 to December 2010. The results of the analysis show that both exchange rate and KOSPI have impacts on the demands for outbound travel. In the case of travels to the United States of America and Philippines, Korean tourists usually have particular purposes such as studying, visiting relatives, playing golf or honeymoon, thus they are less influenced by the exchange rate. Moreover, Korean tourists tend not to visit particular locations for some time when shock reaction happens. As the demands for outbound travels are different from country to country accompanied by economic variables and shock variables, differentiated measure to should be considered to come close to the target numbers of tourists by switching as well as creating the demands. For further study we plan to build outbound tourism forecasting models using Artificial Neural Networks.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.17 no.31
• /
• pp.1-10
• /
• 1994

The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.