• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.28-37
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• 2020

This study developed a deep learning model that predicts rental demand for public bicycles. For this, public bicycle rental data, weather data, and subway usage data were collected. After building an exponential smoothing model, ARIMA model and LSTM-based deep learning model, forecasting errors were compared and evaluated using MSE and MAE evaluation indicators. Based on the analysis results, MSE 348.74 and MAE 14.15 were calculated using the exponential smoothing model. The ARIMA model produced MSE 170.10 and MAE 9.30 values. In addition, MSE 120.22 and MAE 6.76 values were calculated using the deep learning model. Compared to the value of the exponential smoothing model, the MSE of the ARIMA model decreased by 51% and the MAE by 34%. In addition, the MSE of the deep learning model decreased by 66% and the MAE by 52%, which was found to have the least error in the deep learning model. These results show that the prediction error in public bicycle rental demand forecasting can be greatly reduced by applying the deep learning model.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.264-272
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• 2011

The objective of this study was to develop a model for predicting long-term runoff in a basin using the ensemble streamflow prediction (ESP) technique and review its reliability. To achieve the objective, this study improved not only the ESP technique based on the ensemble scenario analysis of historical rainfall data but also conventional ESP techniques used in conjunction with qualitative climate forecasting information, and analyzed and assessed their improvement effects. The model was applied to the Geum River basin. To undertake runoff forecasting, this study tried three cases (case 1: Climate Outlook + ESP, case 2: ESP probability through monthly measured discharge, case 3: Season ESP probability of case 2) according to techniques used to calculate ESP probabilities. As a result, the mean absolute error of runoff forecasts for case 1 proposed by this study was calculated as 295.8 MCM. This suggests that case 1 showed higher reliability in runoff forecasting than case 2 (324 MCM) and case 3 (473.1 MCM). In a discrepancy-ratio accuracy analysis, the Climate Outlook + ESP technique displayed 50.0%. This suggests that runoff forecasting using the Climate Outlook +ESP technique with the lowest absolute error was more reliable than other two cases.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.7-18
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• 2017

Electrical energy is one of the most important energy sources in modern society. Therefore, it is very important to control the supply and demand of electric power. However, the power consumption data needed to predict power demand may include the information about the private behavior of an individual, the analysis of which may raise privacy issues. In this paper, we propose a secure power demand forecasting method where regression analyses on power consumption data are conducted in a trusted execution environment provided by Intel SGX, keeping the power usage pattern of users private. We performed experiments using various regression equations and selected an equation which has the least error rate. We show that the average error rate of the proposed method is lower than those of the previous forecasting methods with privacy protection functionality.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.340-350
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• 2020

A rapid refresh wave forecasting system has been developed using the sea wind on the Korea Local Analysis and Prediction System. We carried out a numerical experiment for wind-wave interaction as an important parameter in determining the forecasting performance. The simulation results based on the seasons of with typhoon and without typhoon has been compared with the observation of the ocean data buoy to verify the forecasting performance. In case of without typhoon, there was an underestimate of overall forecasting tendency, and it confirmed that an increase in the wind-wave interaction parameter leads to a decrease in the underestimate tendency and root mean square error (RMSE). As a result of typhoon season by applying the experiment condition with minimum RMSE on without typhoon, the forecasting error has increased in comparison with the result without typhoon season. It means that the wave model has considered the influence of the wind forcing on a relatively weak period on without typhoon, therefore, it might be that the wave model has not sufficiently reflected the nonlinear effect and the wave energy dissipation due to the strong wind forcing.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.4
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.403-414
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• 2014

Forecasting technology in economic activity is a quite intricate procedure so researchers should grasp the point of the data to use. Diffusion models have been widely used for forecasting market demand and measuring the degree of technology diffusion. However, there is a question that a model, explaining a certain market with goodness of fit, always shows good performance with markets of different conditions. The primary aim of this paper is to explore diffusion models which are frequently used by researchers, and to help readers better understanding on those models. In this study, Logistic, Gompertz and Bass models are used for forecasting Global Wireline Subscribers and the performance of models is measured by Mean Absolute Percentage Error. Logistic model shows better MAPE than the other two. A possible extension of this study may verify which model reflects characteristics of industry better.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.241-248
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• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• Health Policy and Management
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• v.13 no.3
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• pp.91-103
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• 2003

The purpose of this study was to provide basic reference data for stabilization scheme of health expenditure through forecasting of health expenditure. The authors analyzed the health expenditure from 1985 to 2000 that had been calculated by Korean institute for health and social affair using transfer function model as ARIMA model with input series. They used GDP as the input series for more precise forecasting. The model of error term was identified ARIMA(2,2,0) and Portmanteau statics of residuals was not significant. Forecasting health expenditure as percent of GDP at 2010 was 6.8%, under assumption of 5% GDP increase rate. Moreover that was 7.4%, under assumption of 3% GDP increase rate and that was 6.4%, under assumption of 7% GDP increase rate.