• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.884-892
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• 2011

The analysis of the tourist industry in relation to the general industries is of high use as a means to measure an economic effectiveness as the interest in the policy of service industry increases. From the Input-Output Tables of both 2007 and 2008, Inverse Matrix Coefficients, Imports Requirement Coefficients, and Value Added Requirement Coefficients have been derived. As a result of analysis, the main indexes of the industry-related analysis have almost no differences as compared with those of the 1980s. In spite of the reduction in the scope of the tourist industry in this paper, it is estimated that the reason why the above-mentioned result has been derived is that the influence of today's tourist industry grows bigger than that of the past. In the future studies, the agreement on the classification of tourist industry is requested. In addition, all kinds of calculations have to be derived in general, and the general parts of the tourist industry have to be analyzed in details.

• Journal of Agricultural Extension & Community Development
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• v.22 no.3
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• pp.285-292
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• 2015

Recently, the demand of rural tourism has been increased to promote farm household income and rural economy. Korean government has supported to promote rural tourism. One of the most difficult tasks in estimating the economic impact of the tourism industry is how the industry should be defined in terms of an economic sector, since tourism is not defined in national Input-Output (I-O) tables or in the Standard Industrial Classification code. Moreover, there is no specified Standard Industrial Classification for rural tourism. The purpose of the study aims to examine specified Standard Industrial Classification of rural tourism using the I-O model analysis to estimate the economic impacts of rural tourism. Results showed that there were two components considered as inputs. One is the inputs that final demand can move to input of rural tourism in I-O tables. The other is one that the final demand was provided by farm household as intermediate inputs.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.