• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.207-215
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• 2021

The number of tourists visiting Nepal has shown rapid growth in recent years, and Nepal is expecting more tourist arrivals in the future. This paper, thus, attempts to analyze the tourist arrivals in Nepal and predict the number of visitors until 2025. This paper has examined the international tourist arrival trend in Nepal using the Gompertz and Logistic growth model. The international tourist arrival data from 1991 to 2018 is used to investigate international tourist arrival trends. The result of the analysis found that the Gompertz model performs a better fit than the Logistic model. The study further forecast the expected tourist arrival below one million (844,319) by 2025. Nevertheless, the government of Nepal has the goal of two million tourists in a year. The present study also discusses system dynamics scenarios for the two million potential visitors within a year. Scenario analysis shows that proper advertisement and positive word-of-mouth will be key factors in achieving a higher number of tourists. The current study could fill the gap of theoretical and empirical forecasting of tourist arrivals in the Nepalese tourism industry. Also, the study findings would be beneficial for government officers, planners and investors, and policy-makers in the Nepalese tourism industry.

• 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 the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.88-95
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• 2021

In aviation meteorology, the low level wind shear is defined as a sudden change of head windbelow 1600 feet that can affect the departing and landing of the aircraft. Jeju International Airport is an area where low level wind shear is frequently occurred by Mt. Halla. Forecasting of such wind shear would be useful in providing early warnings to aircraft. In this study, we investigated the performance of statistical downscaling model, called Korea Meteorological Administration Post-processing (KMAP) with a 100 m resolution in forecasting wind shear by the complex terrain. The wind shear forecasts was produced by calculating the wind differences between stations aligned with the runways. Two typical wind shear cases caused by complex terrain are validated by comparing to Low Level Wind Shear Alert System (LLWAS). This has been shown to have a good performance for describing air currents caused by terrain.

• Journal of Distribution Science
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• v.19 no.8
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• pp.47-56
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• 2021

Purpose: Maritime sector is fundamental to international trade; there is no doubt that seafarers have played an essential role in maritime shipping and distribution science industry. Thus, this study uses Grey models to predict the number of seafarers in Vietnam expecting to provide a range of future seafarers. Research design, data and methodology: Statistics data are adopted for numbers of seafarers by Vietnam Maritime Administration categorizing into three types: Officers at Management level, Officers at Operational level and Navigation - Engine officer cadet. Results: The results have showed that a lack of qualified seafarers in the distribution industry, which has become a global issue and Vietnam is facing challenges of providing enough supply of seafarers in the next few years. Since there has been a concern of the unbalance between demand and supply of seafarers, researches in maritime sector needs a high accuracy in forecasting the number of available qualified seafarers in Vietnam. Conclusion: This method can be applied to predict numbers of other human resources in transportation, distribution and/or logistics industries when the information is poor and insufficient. The next few years are predicted to witness a downtrend in sailors - oilers which leads to the fact that the total number of available seafarers is decreased.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• Atmosphere
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• v.18 no.4
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• pp.299-316
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• 2008

The intensive upper-air observation network was organized over southwestern region of the Korean Peninsula during the Korea Enhanced Observing Program in 2005 (KEOP-2005). In order to examine the effect of additional upper-air observation on the numerical weather forecasting, three Observing System Experiments (OSEs) using Korea Local Analysis and Prediction System (KLAPS) and Weather Research and Forecasting (WRF) model with KEOP-2005 data are conducted. Cold start case with KEOP-2005 data presents a remarkable predictability difference with only conventional observation data in the downstream and along the Changma front area. The sensitivity of the predictability tends to decrease under the stable atmosphere. Our results indicates that the effect of intensive observation plays a role in the forecasting of the sensitive area in the numerical model, especially under the unstable atmospheric conditions. When the intensive upper-air observation data (KEOP-2005 data) are included in the OSEs, the predictability of precipitation is partially improved. Especially, when KEOP-2005 data are assimilated at 6-hour interval, the predictability on the heavy rainfall showing higher Critical Success Index (CSI) is highly improved. Therefore it is found that KEOP-2005 data play an important role in improving the position and intensity of the simulated precipitation system.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.205-211
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• 2020

The method of predicting the future may be predicted by technical characteristics or technical performance. Therefore, technology prediction is used in the field of strategic research that can produce economic and social benefits. In this study, we predicted the future market through the study of how to predict the future with these technical characteristics. The future prediction method was studied through the prediction of the time when the market occupied according to the demand of special product. For forecasting market demand, we proposed the future forecasting model through comparison of representative quantitative analysis methods such as CAGR model, BASS model, Logistic model and Gompertz Growth Curve. This study combines Rogers' theory of innovation diffusion to predict when products will spread to the market. As a result of the research, we developed a methodology to predict when a particular product will mature in the future market through the spread of various factors for the special product to occupy the market. However, there are limitations in reducing errors in expert judgment to predict the market.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.83-90
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• 2011

The purpose of this study is to develop a series of trend analysis models for industrial water consumption and to propose a blueprint for the integration of the developed models with GIS. For the consumption data acquisition, a real-time sensing technique was adopted. Data were transformed from the field equipments to the management server in every 5 minutes. The data acquired were substituted to a polynomial formula selected. As a result, a series of models were developed for the consumption of each day. A series of validation processes were applied to the developed models and the models were finalized. Then the finalized models were transformed to the average models representing a day's average consumption or an average daily consumption of each month. Demand pattern analyses were fulfilled through the visualization of the finally derived models. It has founded out that the demand patterns show great consistency and, therefore, it is concluded that high probability of demand forecasting for a day or for a season is available. Also proposed is the integration with GIS as an IT tool by which the developed forecasting models are utilized.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5290-5299
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• 2011

The ultimate purpose of this research is to propose a method to improve water supply management efficiency. As an effort to solve this comprehensive problem, the purposes of this paper are summarized into the following two main subjects. One is the development of a series of demand forecasting models targeting for each theme of urban water such as residential, commercial, industrial water. The other is the suggestion on the development and utilization plan of a GIS-based information system where the developed models are incorporated. For these, a series of efforts were performed such as evaluating and choosing of the candidate field areas, selecting a proper sensor and an installation point for each theme. Installed are sensors, a wireless communication infrastructure, and a field data acquisition and management server. Developed are a protocol for the wireless communication and a real-time data monitoring system. Nextly, the urban water facility-related and other necessary data were handled to make those into a series of GIS-ready databases. Finally, a GIS-based management system was designed and a blueprint for the implementation is suggested.