• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.

• Atmosphere
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• v.33 no.5
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• pp.441-456
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• 2023

The mobile observation method, in which a meteorological drone observes while ascending, can observe the vertical profile of wind at 1 m-interval. In addition, since continuous flights are possible at time intervals of less than 30 minutes, high-resolution observation data can be obtained both spatially and temporally. In this study, we verify the accuracy of mobile observation data from meteorological drone (drone) and fill the spatio-temporal observation gaps in the lower atmosphere. To verify the accuracy of mobile observation data observed by drone, it was compared with rawinsonde observation data. The correlation coefficients between two equipment for a wind speed and direction were 0.89 and 0.91, and the root mean square errors were 0.7 m s^-1 and 20.93°. Therefore, it was judged that the drone was suitable for observing vertical profile of the wind using mobile observation method. In addition, we attempted to resolve the observation gaps in the lower atmosphere. First, the vertical observation gaps of the wind profiler between the ground and the 150 m altitude could be resolved by wind observation data using the drone. Secondly, the temporal observation gaps between 3-hour interval in the rawinsonde was resolved through a drone observation case conducted in Taean-gun, Chungcheongnam-do on October 13, 2022. In this case, the drone mobile observation data every 30-minute intervals could observe the low-level jet more detail than the rawinsonde observation data. These results show that the mobile observation data of the drone can be used to fill the spatio-temporal observation gaps in the lower atmosphere.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.37-37
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• 2023

There is currently a high-accuracy modern forecasting method that uses machine learning algorithms or artificial neural network models to forecast river water levels or flowrate. As a result, this study aims to develop a mathematical model based on artificial neural networks to effectively forecast river water levels upstream of Tranh Culvert in North Vietnam's Bac Hung Hai irrigation system. The mathematical model was thoroughly studied and evaluated by using hydrological data from six gauge stations over a period of twenty-two years between 2000 and 2022. Furthermore, the results of the developed model were also compared to those of the long-short-term memory neural networks model. This study performs four predictions, with a forecast time ranging from 6 to 24 hours and a time step of 6 hours. To validate and test the model's performance, the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error, and root mean squared error were calculated. During the testing phase, the NSE of the model varies from 0.981 to 0.879, corresponding to forecast cases from one to four time steps ahead. The forecast results from the model are very reasonable, indicating that the model performed excellently. Therefore, the proposed model can be used to forecast water levels in North Vietnam's irrigation system or rivers impacted by tides.

• MALSORI
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• no.51
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• pp.137-149
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• 2004

A weather forecast service with speech recognition is described. This service allows users to get the weather information of all the cities by saying the city names with just one phone call, which was not provided in the previous weather forecast service. Speech recognition is implemented in the intelligent peripheral (IP) of the advanced intelligent network (AIN). The AIN is a telephone network architecture that separates service logic from switching equipment, allowing new services to be added without having to redesign switches to support new services. Experiments in speech recognition show that the recognition accuracy is 90.06% for the general users' speech database. For the laboratory members' speech database, the accuracies are 95.04% and 93.81%, respectively in simulation and in the test on the developed system.

• Atmosphere
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• v.14 no.3
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• pp.41-54
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• 2004

In this study, a brief overview on a WMO/WWRP program - The Observing System Research and Predictability Experiment (THORPEX) and discussions on perspectives and potential benefits of Asian countries are provided. THORPEX is aimed at accelerating improvements in the accuracy of 1 to 14-day high-impact weather forecasts with research objectives of: 1) predictability and dynamical processes; 2) observing systems; 3) data assimilation and observing strategies; and 4) societal and economic applications. Direct benefits of Asian countries from THORPEX include improvement of: 1) forecast skills in global models, which exerts positive impact on mesoscale forecasts; 2) typhoon forecasts through dropwindsonde observations; and 3) forecast skills for high-impact weather systems via increased observations in neighboring countries. Various indirect benefits for scientific researches are also discussed. Extensive adaptive observation studies are recommended for all high-impact weather systems coming into the Korean peninsula, and enhancement of observations in the highly sensitive regions for the forecast error growth is required to improve forecast skills in the peninsula, possibly through international collaborations with neighboring countries.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.967-974
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• 2011

Based on daily time series from RDAPS numerical weather forecast, Streamflow prediction was simulated and the result of ESP analysis was implemented considering quantitative mid- and long-term forecast to compare the results and review applicability. The result of ESP, ESP considering quantitative weather forecast, and flow forecast from RDAPS numerical weather forecast were compared and analyzed with average observed streamflow in Guem River Basin. Through this process, the improvement effect per method was estimated. The result of ESP considering weather information was satisfactory relatively based on long-term flow forecast simulation result. Discrepancy ratio analysis for estimating accuracy of probability forecast had similar result. It is expected to simulate more accurate flow forecast for RDAPS numerical weather forecast with improved daily scenario including time resolution, which is able to accumulate 3 hours rainfall or continuous simulation estimation.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.80.1-80.1
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• 2016

There are probabilistic forecast models for solar flare occurrence, which can be evaluated by various skill scores (e.g. accuracy, critical success index, heidek skill score, and true skill score). Since these skill scores assume that two types of forecast errors (i.e. false alarm and miss) are equal or constant, which does not take into account different situations of users, they may be unrealistic. In this study, we make an evaluation of a probabilistic flare forecast model [Lee et al., 2012] which use sunspot groups and its area changes as a proxy of flux emergence. We calculate daily solar flare probabilities from 2011 to 2014 using this model. The skill scores are computed through contingency tables as a function of forecast probability, which corresponds to the maximum skill score depending on flare class and type of a skill score. We use a value score with cost/loss ratio, relative importance between the two types of forecast errors. The forecast probability (y) is linearly changed with the cost/loss ratio (x) in the form of y=ax+b: a=0.88; b=0 (C), a=1.2; b=-0.05(M), a=1.29; b=-0.02(X). We find that the forecast model has an effective range of cost/loss ratio for each class flare: 0.536-0.853(C), 0.147-0.334(M), and 0.023-0.072(X). We expect that this study would provide a guideline to determine the probability threshold and the cost/loss ratio for space weather forecast.