• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.575-584
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• 2005

The judgement model to warn of possible pollution accident is constructed by multi-perceptron, multi layer neural network, neuro-fuzzy and it is trained stability, notice, and warming situation due to developed standard axis. The water quality forecasting model is linked to the runoff forecasting model, and joined with the judgement model to warn of possible pollution accident, which completes the artificial intelligence warning system. And GUI (Graphic User Interface) has been designed for that system. GUI screens, in order of process, are main page, data edit, discharge forecasting, water quality forecasting, warming system. The application capability of the system was estimated by the pollution accident scenario. Estimation results verify that the artificial intelligence warning system can be a reasonable judgement of the noized water pollution data.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 1999.04a
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• pp.42.2-42
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• 1999

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.182-192
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• 2008

In oder to develop a decision supporting system for oil spill response, the prototype of pollution response support system which has integrated oil spill prediction system and pollution risk prediction system has developed for Incheon-Daesan area. Spill prediction system calculates oil spill aspects based on real-time wind data and real-time water flow and the residual volume of spilt oil and spread pattern are calculated considering the characteristic of spilt oil. In this study, real-time data is created from results of real-time meteorological forecasting model(National Institute of Environmental Research) using ftp, real-time tidal currents datasets are built using CHARRY(Current by Harmonic Response to the Reference Yardstick) model and real-time wind-driven currents are calculated applying the correlation function between wind and wind-driven currents. In order to model the feature which is spilt oil spreading according to real-time water flow is weathered, the decrease ratio by oil kinds was used. These real-time data and real-time prediction information have been integrated with ESI(Environmental Sensitivity Index) and response resources and then these are provided using GIS as a whole system to make the response strategy.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.15-25
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• 2012

This study is to develop a web-based real-time agricultural flood management system(RAFMS) for 378 agricultural reservoirs equipped with auto water level gauge stations. The RAFMS was designed to operate linking with Rural Agricultural Water Resource Information System(RAWRIS) which supports data viz. real-time rainfall and water level necessary for RAFMS. The system was constituted to monitor the floods simultaneously at each reservoir by calculating the real-time reservoir inflow from watersheds, water level, and release to downstream. In addition, the system has the prediction function for the flood by applying weather forecasting data from Korea Meteorological Administration(KMA).

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.69-79
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• 2017

In this study, we developed real-time urban stream discharge forecasting model using short-term rainfall forecasts data simulated by a regional climate model (RCM). The National Centers for Environmental Prediction (NCEP) Climate Forecasting System (CFS) data was used as a boundary condition for the RCM, namely the Global/Regional Integrated Model System(GRIMs)-Regional Model Program (RMP). In addition, we make ensemble (ESB) forecast with different lead time from 1-day to 3-day and its accuracy was validated through temporal correlation coefficient (TCC). The simulated rainfall is compared to observed data, which are automatic weather stations (AWS) data and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B43; 3 hourly rainfall with $0.25^{\circ}{\times}0.25^{\circ}$ resolution) data over midland of Korea in July 26-29, 2011. Moreover, we evaluated urban rainfall-runoff relationship using Storm Water Management Model (SWMM). Several statistical measures (e.g., percent error of peak, precent error of volume, and time of peak) are used to validate the rainfall-runoff model's performance. The correlation coefficient (CC) and the Nash-Sutcliffe efficiency (NSE) are evaluated. The result shows that the high correlation was lead time (LT) 33-hour, LT 27-hour, and ESB forecasts, and the NSE shows positive values in LT 33-hour, and ESB forecasts. Through this study, it can be expected to utilizing the real-time urban flood alert using short-term weather forecast.

• Water for future
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• v.29 no.1
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• pp.151-161
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• 1996

The proposed flood forecasting system combines a flood routing model with a parameter estimation model. In the parameter estimation model system states and parameters are treated with the extended state-space formulation. The extended Kalman filter is adopted to estimate the states and parameters. A sensitivity analysis is used to investigate the relative significance of the parameters. Insensitive parameters are treated as constants and parameters that are mutually correlated are combined in a simplified form. The developed estimation methodology is applied todam sites of the multi-purpose reservoirs in Korea. The forecasted hydrographs from the extended Kalman filter satisfactorily coincide with the observed. From the time sequence plots of estimated parameters, it is found that the storage coefficient is almost constant, but exponent varies appreciably in time.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.109-120
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• 2006

Since the 1st of July, 2004, the public transport system of the Seoul metropolitan area has been rearranged. In the new system, bus lines are divided into 4 classes-wide area, arterial road, branch, and rotation lines with renewed fare system based on the total distance travelled. Since central control center known as the Bus Management System (BMS) integrates the entire system operation. it now becomes feasible to collect travel information and provide it to the users scientifically and systematically. The Purpose of this study is to forecast transit travel time using real-time traffic data coming from both buses and subway. This is significant contribution since provision of real-time transit information and easy access to it would most likely boost the use of mass transit system, alleviating roadway congestion in the metropolitan area.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2470-2478
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• 1999

In this paper, algorithm is proposed to forecast line utilization using SNMP MIB-II. We calculate line utilization using SNMP MIB-II on TCP/IP based Internet and suggest a method for forecasting a line utilization on the basis of past line utilization. We use a MA model taking difference transform among ARIMA methods. A system for orecasting is proposed. To show availability of this algorithm, some results are shown and analyzed about routers on real environments. We get a future line utilization using this algorithm and compare it ot real data. Correct results are obtained in case of being few data deviating from mean value. This algorithm for forecasting line utilization can give effect to line c-apacity plan for a manager by forecasting the future status of TCP/IP network. This will also help a network management of decision making of performance upgrade.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.886-890
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• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.