• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.13-34
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• 2022

Soil moisture data have been collected at 11 agrometeorological stations operated by The Korea Meteorological Administration (KMA). This study aimed to verify the accuracy of soil moisture data of KMA and develop a correction formula to be applied to improve their quality. The soil of the observation field was sampled to analyze its physical properties that affect soil water content. Soil texture was classified to be sandy loam and loamy sand at most sites. The bulk density of the soil samples was about 1.5 g/cm³ on average. The content of silt and clay was also closely related to bulk density and water holding capacity. The EnviroSCAN model, which was used as a reference sensor, was calibrated using the self-manufactured "reference soil moisture observation system". Comparison between the calibrated reference sensor and the field sensor of KMA was conducted at least three times at each of the 11 sites. Overall, the trend of fluctuations over time in the measured values of the two sensors appeared similar. Still, there were sites where the latter had relatively lower soil moisture values than the former. A linear correction formula was derived for each site and depth using the range and average of the observed data for the given period. This correction formula resulted in an improvement in agreement between sensor values at the Suwon site. In addition, the detailed approach was developed to estimate the correction value for the period in which a correction formula was not calculated. In summary, the correction of soil moisture data at a regular time interval, e.g., twice a year, would be recommended for all observation sites to improve the quality of soil moisture observation data.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.128-139
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• 2022

This study aimed to investigate the tectonic setting of the volcanic edifice at Mt. Baekdu by analyzing petrochemical characteristics of Holocene felsic volcanic rocks distributed in the Baekdusan stratovolcano edifice and summit of the Cheonji caldera rim, as well as Pleistocene mafic rocks of the Gaema lava plateau and Changbaishan shield volcano edifice. During the early eruption phases, mafic eruption materials, with composition ranging from alkali basalt to trachybasalt, or from subalkaline (tholeiitic) basalt to basaltic andesite formed the Gaema lava plateau and Changbaishan shield volcanic edifice, whereas the Baekdusan stratovolcano edifice and Holocene tephra deposits near the summit of the Cheonji caldera comprises trachytic and rhyolitic compositions. Analysis results revealed bimodal compositions with a lack of 54-62 SiO₂, between the felsic and mafic volcanic rocks. This suggested that magmatic processes occurred at the locations of extensional tectonic settings in the crust. Mafic volcanic rocks were plotted in the field of within-plate volcanic zones or between within-plate alkaline and tholeiite zones on the tectonic discrimination diagram, and it was in good agreement with the results of the TAS diagram. Felsic volcanic rocks were plotted in the field of within-plate granite tectonic settings on discrimination diagrams of granitic rocks. None of the results were plotted in the field of arc islands or continental margin arcs. The primitive mantle-normalized spider diagram did not show negative (-) anomalies of Nb and Ti, which are distinctive characteristics of subduction-related volcanic rocks, but exhibited similar patterns of ocean island basalt. Trace element compositions showed no evidence of, magmatic processes related to subduction zones, indicating that the magmatic processes forming the Baekdusan volcanic field occurred in an intraplate environment. The distribution of shallow earthquakes in this region supports the results. The volcanic rocks of the Baekdusan volcanic field are interpreted as the result of intraplate volcanism originating from the upwelling of mantle material during the Cenozoic era.

• Atmosphere
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• v.33 no.2
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• pp.223-246
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• 2023

This paper summarized the research papers on weather extremes that occurred in the Republic of Korea, which were published in the domestic and foreign journals during 1963~2022. Weather extreme is defined as a weather phenomenon that causes serious casualty and property loss; here, it includes typhoon, heavy rain, drought, heat wave, cold surge, heavy snow, and strong gust. Based on the 2011~2020 statistics in Korea, above 80% of property loss due to all natural disasters were caused by typhoons and heavy rainfalls. However, the impact of the other weather extremes can be underestimated rather than we have actually experienced; the property loss caused by the other extremes is hard to be quantitatively counted. Particularly, as global warming becomes serious, the influence of drought and heat wave has been increasing. The damages caused by cold surges, heavy snow, and strong gust occurred over relatively local areas on short-term time scales compared to other weather hazards. In particularly, strong gust accompanied with drought may result in severe forest fires over mountainous regions. We hope that the present review paper may remind us of the importance of weather extremes that directly affect our lives.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Nutrition and Health
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• v.56 no.4
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• pp.439-453
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• 2023

Purpose: Concerns about accelerated aging are regularly being discussed. This study analyzed the dietary status and nutritional intake of college students who are about to enter their 30s and 40s. We further suggest ways to eat healthily. Methods: Totally, 67 students attending Daegu Catholic University were provided with a three-day meal record and analyzed. Results: The average demographics of the subjects were as follows: age 23.2 ± 2.0 years, height 165.1 ± 9.0 cm, weight 65.4 ± 13.6 kg, and BMI 23.5 ± 3.9 kg/m². Overall, 76.3% of the subjects skipped breakfast. The food intake evaluation according to the food group intake pattern and the recommended number of servings determined that 38.3% of the subjects did not consume fruits and dairy products (GMVFDS = 111001), and both men and women lacked all food groups. Analysis of the nutrient intake state revealed lesser nutrient intake than the estimated energy need. Evaluation of the %RDA found that consumption of phosphorus was above the recommended amount, whereas all other nutrients were below the recommended amount. Men had a higher intake rate of phosphorus (p < 0.01), iron (p < 0.001), and sodium (p < 0.05) than women, whereas the intake rate of dietary fiber was higher in women (p < 0.05). Evaluation of the food intake by 22 food categories revealed that intake of regular grains was more in men than in women (p < 0.05) whereas women consumed significantly more mixed grains (p< 0.01). In protein foods, men consumed more meat (p < 0.01), while women consumed more eggs (p < 0.05) and beans (p < 0.05). Evaluating the fruit intake, juices were more frequently consumed by men than by women (p < 0.05). No differences were obtained in food intake and nutrient intake status according to obesity. Conclusion: Based on these results, there is an urgent requirement for attention and support for university cafeterias in order to induce changes in the eating habits of college students. Among other initiatives, this can be achieved by providing diet improvement programs and menus that consider food preferences.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.671-687
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• 2022

It has been reported that about 47% of groundwater wells within 10 km from the coastline in the western/southern coastal areas of Korea were affected by seawater. It has been interpreted that the cause of groundwater salinization is seawater intrusion. The Gilsan stream in the Seocheon area was a tidal stream until the Geumgang estuary dam was constructed and operated. Therefore, it is likely that the Gilsan stream catchment was deposited with sediments containing high-saline formation water prior to the use of landfill farmland at this catchment area. The groundwater in this study area showed EC values ranging from 111 to 21,000 µS/cm, and the water quality types were diverse including Ca(or Na)-HCO₃, Ca(or Na)-HCO₃(Cl), Na-Cl(HCO₃), Na-Cl types. It is believed that this diversity of water quality is due to the mixing of seawater and fresh groundwater generated by infiltration of precipitation and surface water through soil and weathered part. In this study, we discussed whether this water quality diversity and the presence of saline groundwater are due to present seawater intrusion or to remnant high-saline pore water in sediments during flushing out process. For this, rain water, surface water, seawater, and groundwater were compared regarding the water quality characteristics, tritium content, oxygen/hydrogen stable isotopic composition, and ⁸⁷Sr/⁸⁶Sr ratio. The oxygen/hydrogen stable isotopic compositions indicated that water composition of saline groundwaters with large EC values are composed of a mixture of those of fresh groundwater and surface water. Also, the young groundwater estimated by tritium content has generally higher NO₃ content. All these characteristics showed that fresh groundwater and surface water have continued to affect the high-saline groundwater quality in the study area. In addition, considering the deviation pattern in the diagrams of Na/Cl ratio versus Cl content and SAR (sodium adsorption ratio) versus Cl content, in which two end members of fresh surface-ground water and seawater are assumed, it is interpreted that the groundwater in the study area is not experiencing present seawater intrusion, but flush out and retreating from ancient saline formation water.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.517-527
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• 2023

This study aimed to examine the possibility of upcycling extracts of Angelica keiskei and Oenanthe javanica juice by-products through comparing enzyme extraction (EE) and complex extraction (CE) methods to increase the extraction yield and flavor of materials. A higher extraction yield was obtained for free amino acid content with EE and CE for A. keiskei and O. javanica juice by-products, respectively, and a higher extraction efficiency was achieved with juice by-products than with extracts prepared from raw materials before juice production. The content of major amino acids varied depending on the extraction method used. When used according to the characteristics of the extract, their use as a functional material was confirmed along with improvement in the flavor of the food. Consistently high extraction yields for organic acid and sugar levels were obtained with CE in A. keiskei and O. javanica juice by-products. The DPPH radical scavenging ability and TPC were consistently high with CE in A. keiskei and O. javanica juice by-products; the increase in extracted content was likely because of the reaction between the ethanol used for CE and the phenolic compounds. However, because the antioxidant capacity of the juice by-product extracts was somewhat lower than that of the extracts from raw materials before juice production, the amount used should be reviewed. The TFC was found to be higher in extracts obtained with EE than with CE for A. keiskei juice by-products; however, no significant difference was observed between EE and CE in the O. javanica juice by-products. Through this study, the taste compounds and antioxidant properties of extracts obtained from juice by-products produced after the production of A. keiskei and O. javanica green juice were analyzed, and the availability of high value-added materials was confirmed. Based on these research results, expanding specific R&D for practical use should be explored.