• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.126-139
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• 2011

In the traditional four-step travel demand models, each step is sequentially conducted following the model estimation at the previous step. The accuracy of the following model is partly dependent on whether the model at the former stage was properly established or not. Therefore, trip generation, which is the first step in this conventional model, has great effects on the modeling process and forecasting results. Linear regression models for trip generation of Seoul Metropolitan Area might increase the forcasting errors, since a variety of land-use characteristics are not considered. Hence, in this study, zonal factors such as socioeconomic and land use variables are included to improve the elaboration of trip generation. Comparing the %RMSE with the existing models, which contain bigger errors in the zones highly based on the secondary and tertiary industries than residence-based, the trip generation models including those variables seem more appropriate overall.

• Journal of the Korean Society for Railway
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• v.17 no.6
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• pp.433-442
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• 2014

This paper evaluates the performance of transit assignment algorithms for urban rail networks. The accuracy of the algorithms is essential not just for travel forecasting but also for the area of applications such as the assessment of road vulnerability and the fare adjustments between train operating companies. Nonetheless, the suitability and caveats for the series of computational steps have not yet been much discussed. This study thus considers the characteristics that are appropriate for investigating Seoul rail travelers using three representative transit assignment algorithms: the optimal strategy algorithm, route choice algorithms, and the Dial's algorithm. Both the theoretical foundation and the empirical performance are examined. The results demonstrate that the Dial's algorithm is superior in terms of the theoretical soundness and the computational efficiency.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.54-59
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• 2007

Recently, Apartment Reconstruction Projects are performing only with the basis of profitability without establishing either certain criteria or standard guideline. In addition, the profitability information contained in a disposal plan tends to be considered as a fixed value, and it is frequently changeable because reconstruction projects have such a long time to complete and many participants with respective interests. As mentioned above, the new approach needs to be developed which covers the limitation of the unvaried one. Consequently, this study focuses on the probability approach considering not only variances that affect the profit, but the relationship between profit and risk, and then is modeling. This study is anticipated to improve the reliability and accuracy of expected value as well as apply to the decision making criteria quantitively about potentially hidden risks in that projects.

• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.173-183
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• 2009

We present a real-time monitoring system for detecting anomalous network events using the entropy. The entropy accounts for the effects of disorder in the system. When an abnormal factor arises to agitate the current system the entropy must show an abrupt change. In this paper we deliberately model the Internet to measure the entropy. Packets flowing between these two networks may incur to sustain the current value. In the proposed system we keep track of the value of entropy in time to pinpoint the sudden changes in the value. The time-series data of entropy are transformed into the two-dimensional domains to help visually inspect the activities on the network. We examine the system using network traffic traces containing notorious worms and DoS attacks on the testbed. Furthermore, we compare our proposed system of time series forecasting method, such as EWMA, holt-winters, and PCA in terms of sensitive. The result suggests that our approach be able to detect anomalies with the fairly high accuracy. Our contributions are two folds: (1) highly sensitive detection of anomalies and (2) visualization of network activities to alert anomalies.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.123-133
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• 2014

It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1035-1044
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• 2008

It is critical to study on data charateristics analysis and prediction for the flood disaster prevention and water quality monitoring because discharge and TOC data in a river channel are strongly nonlinear. Therefore, in the present study, prediction models for discharge, TOC, and TOC load data were developed using approximation component in the last level and detail components segregated by wavelet transform. The results show that the developed model overcame the persistence phenomenon which could be seen from previous models and improved the prediciton accuracy comparing with the previous models. It might be expected that the results from the present study can mitigate flood disaster damage and construct active alternatives to various water quality problems in the future.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.853-862
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• 2018

An adaptive cut-cell grid based 2D inundation analysis model, K-Flood, is developed in this study. Cut cell grid method divides a grid into a flow area and a non-flow area depending the characteristics of the flows. With adaptive mesh refinement technique cut cell method can represent complex flow area using relatively small number of cells. In recent years, the urban inundation modeling using high resolution and fine quality data is increasing to achieve more accurate flood analysis or flood forecasting. K-Flood has potential to simulate such complex urban inundation using efficient grid generation technique. A finite volume numerical scheme of second order accuracy for space and time was applied. For verification of K-Flood, 1) shockwave reflex simulation by circular cylinder, 2) urban flood experiment simulation, 3) Malpasset dam collapse simulation are performed and the results are compared with observed data and previous simulation results.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.6
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• pp.509-519
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• 2017

Crop simulation models are valuable tools for estimating crop yield, environmental factors and management practices. The objective of this study was to evaluate the effect of soil types on barley productivity using CERES (Crop Environment REsource Synthesis)-barley, cropping system model. So the behavior of the model under various soil types and climatic conditions was evaluated. The results of the sensitivity analysis in temperature, $CO_2$, and precipitation showed that soil types had a direct impact on the simulated yield of CERES-barley model. We found that barley yield in clay soils would be more sensitive to precipitation and $CO_2$ in comparison with temperature. And the model showed limited accuracy in simulating water and nitrogen stress index for soil types. In general, the barley grown on clay soils were less sensitive to water stress than those grown on sandy soils. Especially it was found that the CERES model underestimated the effect of water stress in high precipitation which led to overprediction of crop yield in clay soils. In order to solve these problems and successfully forecast grain yield, further studies on the modification of the water stress response of crops should be considered prior to use of the CERES-barley model for yield forecasting.