• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.7
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• pp.876-885
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• 2017

This study aimed to identify the characteristics of Home Meal Replacement (HMR) product purchases and the need for HMR product development for Hong Kong consumers in order to suggest market segmentation strategies according to consumers' dietary lifestyle. For this, an online survey was conducted on a panel of 521 Hong Kong consumers with HMR purchase experience registered at a specialized organization. Data analysis was performed using SPSS (ver. 23.0). HMR purchase characteristics of Hong Kong consumers according to dietary lifestyle showed significant differences in all items, including 'number of purchases', 'purchase location', 'cost of single purchase', and 'reason for purchase'. According to dietary lifestyle, participants were divided into three clusters: 'High interest', 'normal interest', and 'low interest'. In the case of 'high interest in dietary life group', 'low-sodium food' was the most common, followed by 'heating food', 'low sugar food', and 'low calorie food'. In the case of 'moderate interest in dietary life group', 'low-sodium food' was the most common, followed by 'low sugar food', 'low calorie food', and 'nutritious meal'. In the case of 'low interest in dietary life group', 'low sugar food' was the most common, followed by 'low-sodium food', 'various new menu', and 'easy-to-carry dehydrated food'. For the 'high interest' group, the highest proportion of consumers were male in between the ages of 20 to 29, married, and worked in an office job. The 'high interest' consumers also showed a tendency to pay '15,000 to 20,000 KRW' per single purchase. The 'normal interest' group consisted of an even proportion of male and female consumers, with the most common age range being from 30 to 39 years, and most were married. These consumers preferred to spend 'less than 10,000 KRW' or '10,000 KRW to 15,000 KRW' per single purchase, which is in the lower price range for HMR purchases. The 'low interest in dietary life group' had more females gender-wise, were unmarried, and worked in an office job, For a single purchase, the 'low interest' group chose to pay less than 10,000 KRW, which is relatively lower than the other two clusters. The results of this study can be used as baseline data for building marketing strategies for HMR product development. It can also provide basic data and directions for new HMR export products that reflect consumer needs in order to create a market segmentation strategy for industrial applications.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.4
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• pp.330-344
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• 2015

Semi-continuous measurements of $PM_{2.5}$ mass, organic and elemental carbon were made for the period of January to October 2014, at six national air monitoring stations in Korea. OC and EC concentrations showed a clear seasonal variation with the highest in winter (January) and the lowest in summer (August). In winter, the high carbonaceous concentrations were likely influenced by increased fuel combustion from residential heating. OC and EC concentrations varied by monitoring stations with 5.9 and $1.7{\mu}g/m^3$ in Joongbu area, 4.2 and $1.2{\mu}g/m^3$ in Honam area, 4.0 and $1.3{\mu}g/m^3$ in Yeongnam area, 3.7 and $1.6{\mu}g/m^3$ in Seoul Metropolitan area, 3.0 and $0.8{\mu}g/m^3$ in Jeju Island, 2.9 and $0.7{\mu}g/m^3$ in Baengnyeong Island respectively. The concentrations of OC and EC comprised 9.6~ 15.5% and 2.4~ 4.7% of $PM_{2.5}$. Urban Joongbu area located adjacent to the intersection of several main roads showed the highest carbon concentration among six national air monitoring station. On the other hand, background Baengnyeong Island showed the lowest carbon concentration and the highest OC/EC ratio (4.5). During the haze episode, OC and EC were enhanced with increase in $PM_{2.5}$ about 1.3~ 3 and 1.3~ 4.0 times respectively. The concentrations of OC, EC in the Asian dust case are about 1~ 2.4 times greater than in the nondust case. The origins of air mass pathways arriving at Seoul, using the backward trajectory analysis, can be mostly classified into 6 groups (Sector I Northern Korea including the sea of Okhotsk, Sector II Northern China including Mongolia, Sector III Southern China, Sector IV South Pacific area, Sector V Japan, Sector VI Southern Korea area). When an air mass originating from northern China and Mongolia, the OC concentrations were the most elevated, with a higher OC/EC ratio (2.4~ 3.3), and accounting for 17% of $PM_{2.5}$ mass on average.