• 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.

• Atmosphere
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• v.26 no.1
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.353-358
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• 2001

Since the financial crisis broke out, liquidity has become the critical issue in housing construction industry. In order to secure liquidity, it is prerequisite to precisely forecast cash flow. However, construction companies have failed to come up with a systematic process to manage and forecast cash flow. Until now, companies have solely relied on the prediction of profits and losses, which is carried out as they review business feasibility. To obtain more accurate cash flow forecast model, practical pattern of payments should be taken into account. In this theory, basic model that analyzes practical housing payment collection pattern resulting from prepayments and arrears is described. This model is to complement conventional cash flow forecast scheme in the phase of business feasibility review. Analysis result on final losses in cash that occur as a result of prepayment and arrears is considered in this model. Additionally, in the estimation of construction cost in the phase of business feasibility review, real construction prices instead of official prices are applied to enhance accuracy of cash outflow forecast. The proportion of payment made by a bill and changes in payment date caused by rescheduling of a bill are also factored in to estimate cash outflow. This model would contribute to achieving accurate cash flow forecast that better reflect real situation and to enhancing efficiency in capital management by giving a clear picture with regard to the demand and supply timing of capital.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.67-75
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• 2012

Different from other natural hazards including floods, drought advances slowly and spreads widely, so that the preparedness is quite important and effective to mitigate the impacts from drought. Evaluation and forecast the status of drought for the present and future utilizing the meteorological scenario for agricultural drought can be useful to set a plan for agricultural drought mitigation in agriculture water resource management. In this study, drought climate scenario model on the basis of historical drought records for preparing agricultural drought mitigation was developed. To consider dependency and correlation between various climate variables, this model was utilized the historical climate pattern using reference year setting of four drought levels. The reference year for drought level was determined based on the frequency analysis result of monthly effective rainfall. On the basis of this model, drought climate scenarios at Suwon and Icheon station were set up and these scenarios were applied on the water balance simulation of reservoir water storage for Madun reservoir as well as the soil moisture model for Gosam reservoir watershed. The results showed that drought climate scenarios in this study could be more useful for long-term forecast of longer than 2~3 months period rather than short-term forecast of below one month.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1814-1820
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• 2007

As RFID/USN has appeared as one aspect of the latest trend in Convergence rapidly spreading over the society, the use and the applied service of RFID/USN are expected to be diverse. USN begins by the technological means to providing the new type of service, and provides the customers with the factors in various type of its application to satisfy their desire. This thesis suggests a forecast model of the u-Distribution with expecting the new change of the new generation of distribution by using RFID/USN, which is initiatively introduced into this area. For the suggestion of the forecast model, the way of planning of it will be discussed by examining the necessity of the model. Also, the systematic and technical parts of the new forecast model will be schematized.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.92-101
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• 2016

Objectives: Outdoor workers are exposed to thermally stressful work environments. In this study, heat stress indices for harbor workers in summer were calculated to evaluate thermal comfort based on a human heat balance model. These indices are Physiological Subjective Temperature (PST), Dehydration Risk (DhR), and Overheating Risk (OhR) according to respective stage of cargo work in a harbor. In addition, we constructed a forecast system to provide heat stress information. Methods: Thermophysiological indices in this study were calculated using the MENEX model (i.e. the human heat balance model), which used as inputs the meteorological parameters, clothing insulation, and metabolic rate for each stage of cargo work in the harbor of Masan over the course of seven days, including a four-day heat wave. The forecast heat stress information constructed for Masan harbor was based on meteorological data supported by the Dong-Nae Forecast from the KMA (Korea Metrological Administration) and other input parameters. Results: According to higher metabolic rate, thermophysiological indices showed a critical level. In particular, PST was evaluated as reaching the 'Very hot' or 'Hot' level during all seven days, despite the heat occurring over only four. It is important in a regard to consider the work environment conditions (i.e. labor intensity and clothing in harbor). On a webpage, the forecast thermophysiological indices show as infographics to be easily understand. This webpage is comprised of indices for both current conditions and the forecast, with brief guidance. Conclusion: Thermophysiological indices show the risk level to health during a heat wave period. Heat stress information could help to protect the health of harbor workers. Further, this study could extend the applicability of these indices to a variety of outdoor workers in consideration of work environments.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.