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Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data. [ABSTRACT FROM AUTHOR]

We construct a continuous 24-h daily fine particulate matter (PM2.5)record with 2×2 km2 resolution over eastern China, South Korea, and Japan for 2011–2022 by applying a random forest (RF) algorithm to aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) I and II satellite instruments. The RF uses PM2.5 observations from the national surface networks as training data. PM2.5 network data starting in 2015 in South Korea are extended to pre-2015 with a RF trained on other air quality data available from the network including PM10. PM2.5 network data starting in 2014 in China are supplemented by pre-2014 data from the US embassy and consulates. Missing AODs in the GOCI data are gap-filled by a separate RF fit. We show that the resulting GOCI PM2.5 dataset is successful in reproducing the surface network observations including extreme events, and that the network data in the different countries are representative of population-weighted exposure. We find that PM2.5 peaked in 2014 (China) and 2013 (South Korea, Japan), and has been decreasing steadily since with no region left behind. We quantify the population in each country exposed to annual PM2.5 in excess of national ambient air quality standards and how this exposure evolves with time. The long record for the Seoul Metropolitan Area (SMA) shows a steady decrease from 2013 to 2022 that was not present in the first five years of AirKorea network PM2.5 measurements. Mapping of an extreme pollution event in Seoul with GOCI PM2.5 shows a predicted distribution indistinguishable from the dense urban network observations, while our previous 6×6 km2 product smoothed local features. Our product should be useful for public health studies where long-term spatial continuity of PM2.5 information is essential. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. Results: The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Conclusion: Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care. [ABSTRACT FROM AUTHOR]

In this study, we predict the habitat suitability of Thuja koraiensis, an endemic plant species on the Korean Peninsula, and establish management strategies for its conservation. To achieve this, field surveys were conducted on the Korean Peninsula and northeastern China to determine species occurrence points. An ensemble modeling approach was implemented, combining the Random Forest, Generalized Boosted Model, and Generalized Linear Model algorithms, to explore suitable habitats. Our results show that under current climate conditions, wide suitable habitats can be found in South Korea. Furthermore, our analysis indicated that elevation had the greatest impact on the availability of suitable habitats. On the basis of the shared socioeconomic pathway data presented in the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report, changes in future habitat distribution were observed for the 2050s (2040-2060) and 2070s (2060-2080). The results indicated a significant decrease in the availability of suitable habitats in South Korea, while relatively wide suitable habitats were observed in North Korea. In particular, the elevation of suitable habitat areas gradually increased, suggesting that future suitable habitats may become isolated. These research findings substantiate the necessity for implementing appropriate measures to conserve and manage the climate-vulnerable species T. koraiensis to ensure its long-term preservation and sustainable management. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of ML models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg−1, which favor enhanced organic matter in the soil. Different models identified different impacts of macronutrients on the organic matter content in the soil. The SHAP method is practical for assessing whether different ML models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances. [ABSTRACT FROM AUTHOR]

We investigated a screening method for mild cognitive impairment (MCI) that combined bioimpedance features and the Korean Mini-Mental State Examination (K-MMSE) score. Data were collected from 539 subjects aged 60 years or older at the Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, A total of 470 participants were used for the analysis, including 318 normal controls and 152 MCI participants. We measured bioimpedance, K-MMSE, and the Seoul Neuropsychological Screening Battery (SNSB-II). We developed a multiple linear regression model to predict MCI by combining bioimpedance variables and K-MMSE total score and compared the model's accuracy with SNSB-II domain scores by the area under the receiver operating characteristic curve (AUROC). We additionally compared the model performance with several machine learning models such as extreme gradient boosting, random forest, support vector machine, and elastic net. To test the model performances, the dataset was divided into a training set (70%) and a test set (30%). The AUROC values of SNSB-II scores were 0.803 in both sexes, 0.840 for males, and 0.770 for females. In the combined model, the AUROC values were 0.790 (0.773) for males (and females), which were significantly higher than those from the model including MMSE scores alone (0.723 for males and 0.622 for females) or bioimpedance variables alone (0.640 for males and 0.615 for females). Furthermore, the accuracies of the combined model were comparable to those of machine learning models. The bioimpedance-MMSE combined model effectively distinguished the MCI participants and suggests a technique for rapid and improved screening of the elderly population at risk of cognitive impairment. [ABSTRACT FROM AUTHOR]

Since the WHO's 2021 aging redefinition emphasizes "healthy aging" by focusing on the elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008–2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population. [ABSTRACT FROM AUTHOR]

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%. [ABSTRACT FROM AUTHOR]

The damage caused by fire accidents is increasing worldwide. In particular, when a fire occurs, property damage directly affects the lives of citizens. Therefore, in this study, machine learning techniques were applied to analyze the prediction of the future amount of property damage from fire as well as the fire occurrence factors within a geographic area. To achieve this, three years of spatially distributed fire big data for Seoul, the capital of Korea, was used. For the predictive analysis of the amount of fire property damage, the results of analysis by applying machine learning techniques through k-fold cross-validation were calculated. As part of these results, when predicting and analyzing the amount of fire property damage using the random forest (RF) algorithm, an accuracy of 83% was calculated by comparing the predicted data with the actual data. On this basis, the importance of the fire risk factors was analyzed, and it was found that the main factor in the occurrence of fires is the condition of the facilities inside apartment houses. The findings of this study are expected to be used as an important guide for identifying property damage by fire and the factors determining the occurrence of fires in Korea, enabling the evaluation of their spatial distribution and the application of corrective measures to reduce possible damage by urban fires. [ABSTRACT FROM AUTHOR]

Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common chronic diseases, the elderly tends to have a high incidence of depression. That said, it has been vital to focus on preventing depression in the elderly in advance. Hence, this study aims to select the factors related to depression in low-income seniors identified in previous studies and to develop a prediction model. In this study, 2975 elderly people from low-income families were extracted using the 13th-year data of the Korea Welfare Panel Study (2018). Decision trees, logistic regression, neural networks, and random forest were applied to develop a predictive model among the numerous data mining techniques. In addition, the wrapper's stepwise backward elimination, which finds the optimal model by removing the least relevant factors, was applied. The evaluation of the model was confirmed via accuracy. It was verified that the final prediction model, in the case of a decision tree, showed the highest predictive power with an accuracy of 97.3%. Second, psychological factors, leisure life satisfaction, social support, subjective health awareness, and family support ranked higher than demographic factors influencing depression. Based on the results, an approach focused on psychological support is much needed to manage depression in low-income seniors. As predicting depression in the elderly varies on numerous influencing factors, using a decision tree may be beneficial to establish a firm prediction model to identify vital factors causing depression in the elderly population. [ABSTRACT FROM AUTHOR]

Simple Summary: Efforts to prevent snakebites on hiking trails are crucial because such accidents can lead to fatalities, particularly in isolated areas. This study developed random forest species distribution models to predict the potential habitats of Gloydius spp. in South Korea, verified a significant relationship between the habitat distribution and actual snakebite accidents at the regional level, and assessed snakebite risks on trails in the national parks of Korea. The model identified high-risk snakebite areas, which will help to develop preventative solutions for hikers' safety. Snakebites can pose a significant threat to human health as the destruction of natural habitats and increased human intrusion into ecosystems result in more frequent encounters with snakes. Mitigation measures for snakebites are particularly crucial for hiking trails where transportation of snakebite victims to medical facilities is challenging due to limited emergency resources and difficult access. This study employed a random forest-based species distribution model approach to investigate the potential habitats of Gloydius spp., specifically Gloydius saxatilis, Gloydius brevicaudus, and Gloydius ussuriensis, in South Korea and to assess the snakebite risk in national parks. Potential habitats of Gloydius spp. were identified and visualized by overlaying binary maps derived from species distribution models (SDMs) of each Gloydius spp. that corresponded to high-risk snakebite areas. In addition, hiking trails with high snakebite risk in the national parks were identified after demonstrating the statistical correlation between the potential habitat distribution of Gloydius spp. and the actual snakebite incidents in major regions of South Korea. The primary environmental variables determining Gloydius spp. habitat were the topographic position index, slope, and the annual average of the maximum and minimum temperatures. The potential habitat of G. saxatilis generally appeared in high-altitude mountainous areas, mostly in the eastern part of the study area. Favorable habitats for G. brevicaudus and G. ussuriensis were predominantly located in mountainous areas throughout the study area, with the exception of some high-altitude mountainous terrain in the east. The number of snakebite incidents per 10,000 people was significantly correlated with the area ratio of Gloydius spp. potential habitat (Spearman's rho = 0.638, p < 0.01). The proportion of snakebite risk areas among national parks in South Korea ranged from 18% to 57%. This study can support practical solutions to prevent injuries and fatalities among hikers due to snakebites by identifying areas with a high risk of snakebite accidents at the hiking-trail level. [ABSTRACT FROM AUTHOR]

Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units. [ABSTRACT FROM AUTHOR]

The Civilian Access Control Zone (CACZ), south of the Demilitarized Zone (DMZ) separating North and South Korea, has functioned as a unique bio-reserve owing to restrictions on human use. However, it is now increasingly threatened by damaged land and slope failures. In this study, a machine-learning-based method was used to assess slope stability by introducing the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) approaches. These classification models were trained and evaluated on 393 slope stability cases from 2009 to 2019 to assess slope stability in the northern area of the Civilian Control Line, South Korea. For comparison, the performance of these classification models was measured by considering the accuracy, Cohen's kappa, F1-score, recall rate, precision, and area under the ROC curve (AUC). Furthermore, 14 influencing factors (slope, vegetation, structure conditions, etc.) were considered to explore feature importance. The evaluation and comparison of the results showed that the performance of all classifier models was satisfactory for assessing the stability of the slope, the ability of LR was validated (accuracy = 0.847; AUC = 0.838), and XGBoost proved to be the most efficient method for predicting slope stability (accuracy = 0.903; AUC = 0.900). Among the 14 influencing factors, the external condition was the most important. The proposed supervised learning method offers a promising method for assessing slope status, may be beneficial for government agencies in early-stage risk mitigation, and provides a database for efficient restoration management. [ABSTRACT FROM AUTHOR]

Aim: To identify the factors affecting Emergency Department Length of Stay for transferred critically ill patients. Background: The Length of Stay of the transferred patients is an important indicator of Emergency Department service quality; thus, understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is essential. Methods: Using the electronic medical records of 968 transferred critically ill Emergency Department patients of a tertiary hospital in Korea, prediction models for Emergency Department Length of Stay were built using various machine learning algorithms. Results: The logistic regression (AUROC 0.85) models showed the best performance, followed by random forest (AUROC 0.83) and Naive Bayes (AUROC 0.83). The logistic regression model indicated that fewer consultations, the highest acuity level, need for an emergency operation or angiography, need for ICU admission, severe emergency disease and fewer diagnoses were the statistically significant predictors for Emergency Department Length of Stay of 6 h or less. Conclusions: The transferred critically ill patients analysed in this study who required immediate or specialized care tended to receive needed care on time at the study site. Implications for Nursing Management: Understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is crucial for developing strategies to manage the nursing resource of Emergency Department successfully. [ABSTRACT FROM AUTHOR]

This study presents the results of an effort to improve the forecast of precipitation (> 0.1 mm/hr or > 0.1 mm/3hr) in the Local Data Assimilation and Prediction System (LDAPS) and the Global Data Assimilation and Prediction System (GDAPS) by applying the Random Forest (RF) model in South Korea. LDAPS and GDAPS are Numerical Weather Prediction (NWP) models operated by the Korea Meteorological Administration (KMA) for weather forecasting. GDAPS operates the Unified Model (UM) and the Korean Integrated Model (KIM). This study used weather forecast data from LDAPS, GDAPS/KIM, and GDAPS/UM. Precipitation forecasts from LDAPS and GDAPS were corrected by RF training with rain gauge observations from about 685 stations. Approximately 35 selected NWP model output variables were used as inputs to the RF training. To reflect recent trends in biases between observations and NWP, the precipitation probability prediction model was designed for real-time learning using a sliding window technique. In addition, the precipitation data had a data imbalance problem with more precipitation cases than non-precipitation cases, so an undersampling method was applied to solve this problem. Comparing the performance of the proposed method with NWP in predicting precipitation, the CSI was improved by 14.7-23.1% (LDAPS), 33.9% (GDAPS/KIM), and 6.7%-38% (GDAPS/UM) over NWP, and the accuracy was also better. In future research, automating the sampling rate selection to reflect recent weather trends when undersampling is likely to improve forecast performance. [ABSTRACT FROM AUTHOR]

Surface ozone pollution in South Korea has increased over the past 2 decades, despite efforts to decrease emissions, and is pervasively in exceedance of the maximum daily 8 h average (MDA8) standard of 60 ppb. Here, we investigate the 2015–2019 trends in surface ozone and NO 2 concentrations over South Korea and the Seoul metropolitan area (SMA), focusing on the 90th percentile MDA8 ozone as an air quality metric. We use a random forest algorithm to remove the effect of meteorological variability on the 2015–2019 trends and find an ozone increase of up to 1.5 ppb a -1 in April–May, while NO 2 decreases by 22 %. Global 3-D atmospheric chemistry model simulations including recent chemical updates can successfully simulate surface ozone over South Korea and China as well as the very high free-tropospheric ozone observed above 2 km altitude (mean 75 ppb in May–June) and can reproduce the observed 2015–2019 emission-driven ozone trend over the SMA including its seasonality. Further investigation of the model trend for May, when meteorology-corrected ozone and its increase are the highest, reveals that a decrease in South Korea NO x emissions is the main driver for the SMA ozone increase. Although this result implies that decreasing volatile organic compound (VOC) emissions is necessary to decrease ozone, we find that ozone would still remain above 80 ppb even if all anthropogenic emissions in South Korea were shut off. China contributes only 8 ppb to this elevated South Korea background, and ship emissions contribute only a few parts per billion. Zeroing out all anthropogenic emissions in East Asia in the model indicates a remarkably high external background of 56 ppb, consistent with the high concentrations observed in the free troposphere, implying that the air quality standard in South Korea is not practically achievable unless this background external to East Asia can be decreased. [ABSTRACT FROM AUTHOR]

Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6's object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system's capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives. [ABSTRACT FROM AUTHOR]

The western conifer seed bug (WCSB; Leptoglossus occidentalis) causes huge ecological and economic problems as an alien invasive species in forests. In this study, a species distribution model (SDM) was developed to evaluate the potential occurrence of the WCSBs and the effects of climate on WCSB distribution in South Korea. Based on WCSB occurrence and environmental data, including geographical and meteorological variables, SDMs were developed with maximum entropy (MaxEnt) and random forest (RF) algorithms, which are machine learning methods, and they showed good performance in predicting WCSB occurrence. On the potential distribution map of WCSBs developed by the model ensemble with integrated MaxEnt and RF models, the WCSB occurrence areas were mostly located at low altitudes, near roads, and in urban areas. Additionally, environmental factors associated with anthropogenic activities, such as roads and night lights, strongly influenced the occurrence and dispersal of WCSBs. Metropolitan cities and their vicinities in South Korea showed a high probability of WCSB occurrence. Furthermore, the occurrence of WCSBs in South Korea is predicted to intensify in the future owing to climate change. [ABSTRACT FROM AUTHOR]

Climate change is altering habitat environments, threatening the survival of Picea jezoensis (Siebold et Zucc.) Carrière in South Korea. Species distribution models (SDMs) have been considered an effective tool to investigate the impact of climate change on species' potential distributions. We collected occurrence data and bioclimatic data to predict the current and future distributions by ensemble distribution modeling on the BIOMOD2 platform. The predicted distribution habitats of P. jezoensis for both current and future climates were shown by ensemble prediction with true skill statistic (TSS) values of over 0.8. Using the random forest (RF) and general linear model (GLM), the natural habitat of P. jezoensis was predicted to become extinct in the 2070s under representative concentration pathway (RCP) 4.5. However, the general additive model (GAM) predicted a limited habitat of P. jezoensis across the South Korean peninsula in the 2070s. Hence, the natural distribution of P. jezoensis is not expected to find alternative habitats to offset their loss of current habitat, placing them at more significant risk of extinction in the future. These findings will aid the effective management and conservation of P. jezoensis by providing an understanding of the future impact of climate change on targeted endangered species. [ABSTRACT FROM AUTHOR]

Odor is a very serious problem worldwide. Thus, odor prediction research has been conducted consistently to help prevent odor. Odor substances that are complex odors are known, but complex odors and odor substances do not have a linear dependence. In addition, depending on the combination of odor substances, the causal relationships, such as synergy and antagonism, are different for complex odors. Research is needed to know this, but the situation is incomplete. Therefore, in this study, research was conducted through data-based research. The complex odor was predicted using various machine learning methods, and the effect of odor substances on the complex odor was verified using an explainable artificial intelligence method. In this study, according to the Malodor Prevention Act in Korea, complex odors are divided into two categories: acceptable and unacceptable. Analysis of variance and correlation analysis were used to determine the relationships between variables. Six machine learning methods (k-nearest neighbor, support vector classification, random forest, extremely randomized tree, eXtreme gradient boosting, and light gradient boosting machine) were used as predictive classification models, and the best predictive method was chosen using various evaluation metrics. As a result, the support vector machine that performed best in five out of six evaluation metrics was selected as the best model (f1-score = 0.7722, accuracy = 0.8101, sensitivity = 0.7372, specificity = 0.8656, positive predictive value = 0.8196, and negative predictive value = 0.8049). In addition, the partial dependence plot method from explainable artificial intelligence was used to understand the influence and interaction effects of odor substances. [ABSTRACT FROM AUTHOR]

Background: The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata. Methods: Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan–Meier (KM) curve was used to visualize the identified strata obtained from the ML models. Results: No significant differences in the input variables were observed between the training and test datasets. The best‐performing models were support vector machine in predicting 1‐year afatinib continuation (AUC 0.626) and decision tree in 2‐year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log‐rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time‐on‐treatment (TOT) and overall survival (OS). Conclusion: The performances of ML models in our study found no discernible roles in predicting afatinib‐related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record‐based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. [ABSTRACT FROM AUTHOR]

Objective There is little evidence for finding optimal antipsychotic treatment for schizophrenia, especially in paediatrics. To evaluate the performance and clinical benefit of several prediction methods for 1-year treatment continuation of antipsychotics. Design and Settings Population-based prognostic study conducting using the nationwide claims database in Korea. Participants 5109 patients aged 2-18 years who initiated antipsychotic treatment with risperidone/ aripiprazole for schizophrenia between 2010 and 2017 were identified. Main outcome measures We used the conventional logistic regression (LR) and common six machinelearning methods (least absolute shrinkage and selection operator, ridge, elstic net, randomforest, gradient boosting machine, and superlearner) to derive predictive models for treatment continuation of antipsychotics. The performance of models was assessed using the Brier score (BS), area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The clinical benefit of applying these models was also evaluated by comparing the treatment continuation rate between patients who received the recommended medication by models and patients who did not. Results The gradient boosting machine showed the best performance in predicting treatment continuation for risperidone (BS, 0.121; AUROC, 0.686; AUPRC, 0.269). Among aripiprazole models, GBM for BS (0.114), SuperLearner for AUROC (0.688) and random forest for AUPRC (0.317) showed the best performance. Although LR showed lower performance than machine learnings, the difference was negligible. Patients who received recommended medication by these models showed a 1.2-1.5 times higher treatment continuation rate than those who did not. Conclusions All prediction models showed similar performance in predicting the treatment continuation of antipsychotics. Application of prediction models might be helpful for evidence-based decision-making in antipsychotic treatment. [ABSTRACT FROM AUTHOR]

The independent and relative importance of ten variables related to fire, site, and prefire vegetation affecting the initial regeneration after fires was analyzed using the Random Forest statistical technique. The technique supplemented with the decision tree method was developed. This study was performed in prefire Pinus densiflora-dominated forests affected by large-scale fires in Korea. Among the ten variables, the basal area of prefire resprouter trees showed an overwhelmingly high contribution on the postfire regeneration (model improvement ratio (MIR) = 1.00). Consequently, stands with a high abundance of the resprouters showed rapid regeneration. Site factors including elevation, aspect, and slope had an MIR of 0.71, 0.63, and 0.57, respectively, while those for flame ratio and burn severity were 0.39 and 0.20. We revealed that the legacy of prefire vegetation had the most significant effect on the initial regeneration of stands, while site-variables played a lesser role, and fire-variables such as burn severity had a relatively minor effect. This technique was an important tool for explaining ecological phenomena involving the simultaneous action of multiple factors. Understanding the priorities of factors affecting the fast regeneration would be useful in establishing a restoration plan in forests where soil erosion is an issue following a fire. [ABSTRACT FROM AUTHOR]

Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites. [ABSTRACT FROM AUTHOR]

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East Asian countries have been conducting source apportionment of fine particulate matter (PM 2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved. [Display omitted] • Missing values of hourly PM 2.5 constituents were estimated using a random forest. • Estimation accuracy (r2) was 0.631–0.874, highest for carbon species. • PMF was performed by replacing missing values in the input data with estimates. • Source contributions were compared to the complete PMF result with percent errors. • Replacing the missing carbon species minimized the percent errors to 2.0%. [ABSTRACT FROM AUTHOR]

In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R2 values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production. [ABSTRACT FROM AUTHOR]

A two-dimensional visibility estimation model was developed, based on random forest (RF), a machine learning-based technique. A geostatistical method was introduced into the visibility estimation model for the first time to interpolate point measurement data to gridded data spatially with a pixel size of 10 km. The RF-based model was trained using gridded visibility data, as well as meteorological and air pollution input variable data, for each location in South Korea, which were characterized by complex geographical features and high air pollution levels. Generally, relative humidity was the most important input variable for the visibility estimation (average mean decrease accuracy: 35%). However, PM2.5 tended to be the most crucial variable in polluted regions. The spatial interpolation was found to result in an additional visibility estimation error of 500 m in locations where no adjacent visibility observations within 0.2° were available. The performance of the proposed model was preliminarily assessed. Generally, the best detection performance was achieved in good visibility conditions (visibility range: 10 to 20 km). This study is the first to demonstrate a visibility estimation model based on a geostatistical method and machine learning, which can provide visibility information in locations for which no observations exist. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

Forecasting wind speed near the surface with high-spatial resolution is beneficial in agricultural management. There is a discrepancy between the wind speed information required for agricultural management and that produced by weather agencies. To improve crop yield and increase farmers' incomes, wind speed prediction systems must be developed that are customized for agricultural needs. The current study developed a high-resolution wind speed forecast system for agricultural purposes in South Korea. The system produces a wind speed forecast at 3 m aboveground with 100-m spatial resolution across South Korea. Logarithmic wind profile, power law, random forests, support vector regression, and extreme learning machine were tested as candidate methods for the downscaling wind speed data. The wind speed forecast system developed in this study provides good performance, particularly in inland areas. The machine learning–based methods give the better performance than traditional methods for downscaling wind speed data. Overall, the random forests are considered the best downscaling method in this study. Root mean square error and mean absolute error of wind speed prediction for 48 h using random forests are approximately 0.8 m/s and 0.5 m/s, respectively. [ABSTRACT FROM AUTHOR]

This study investigated how changes in weather factors affect the prevalence of conjunctivitis using public big data in South Korea. A total of 1,428 public big data entries from January 2013 to December 2019 were collected. Disease data and basic climate/air pollutant concentration records were collected from nationally provided big data. Meteorological factors affecting eye diseases were identified using multiple linear regression and machine learning analysis methods such as extreme gradient boosting (XGBoost), decision tree, and random forest. The prediction model with the best performance was XGBoost (1.180), followed by multiple regression (1.195), random forest (1.206), and decision tree (1.544) when using root mean square error (RMSE) values. With the XGBoost model, province was the most important variable (0.352), followed by month (0.289) and carbon monoxide exposure (0.133). Other air pollutants including sulfur dioxide, PM10, nitrogen dioxides, and ozone showed low associations with conjunctivitis. We identified factors associated with conjunctivitis using traditional multiple regression analysis and machine learning techniques. Regional factors were important for the prevalence of conjunctivitis as well as the atmosphere and air quality factors. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and deep neural network (DNN) techniques are promising tools. These can advance mathematical crop modelling methodologies that can integrate these schemes into a process-based crop model capable of reproducing or simulating crop growth. In this study, an innovative hybrid approach for estimating the leaf area index (LAI) of paddy rice using climate data was developed using ML and DNN regression methodologies. First, we investigated suitable ML regressors to explore the LAI estimation of rice based on the relationship between the LAI and three climate factors in two administrative rice-growing regions of South Korea. We found that of the 10 ML regressors explored, the random forest regressor was the most effective LAI estimator, and it even outperformed the DNN regressor, with model efficiencies of 0.88 in Cheorwon and 0.82 in Paju. In addition, we demonstrated that it would be feasible to simulate the LAI using climate factors based on the integration of the ML and DNN regressors in a process-based crop model. Therefore, we assume that the advancements presented in this study can enhance crop growth and productivity monitoring practices by incorporating a crop model with ML and DNN plans. [ABSTRACT FROM AUTHOR]

A respiratory syndrome COVID-19 pandemic has become a serious public health issue nowadays. The COVID-19 virus has been affecting tens of millions people worldwide. Some of them have recovered and have been released. Others have been isolated and few others have been unfortunately deceased. In this paper, we apply and compare different machine learning approaches such as decision tree models, random forest, and multinomial logistic regression to predict isolation, release, and decease states for COVID-19 patients in South Korea. The prediction can help health providers and decision makers to distinguish the states of infected patients based on their features in early intervention to take an action either by releasing or isolating the patient after the infection. The proposed approaches are evaluated using Data Science for COVID-19 (DS4C) dataset. An analysis of DS4C dataset is also provided. Experimental results and evaluation show that multinomial logistic regression outperforms other approaches with 95% in a state prediction accuracy and a weighted average F1-score of 95%. [ABSTRACT FROM AUTHOR]

Heavy snowfall is a natural disaster that causes extensive damage in South Korea. Therefore, it is crucial to predict snowfall occurrence and establish countermeasures to reduce the damage caused by heavy snowfall. In this study, the meteorological and geographic data of the past 30 years were collected, and four machine learning algorithms were used: multiple linear regression (MLR), support vector regression (SVR), random forest regressor (RFR), and eXtreme gradient boosting (XGB). Subsequently, the performances of the machine learning algorithms were compared. Machine-learning algorithms were selected as regression models to predict heavy snowfall. Additionally, grid search and five-fold cross-validation techniques were used to improve learning performance. Model performance was evaluated by comparing the observed and predicted data. It was observed that the RFR model accurately predicted the occurrence of snowfall (R2=0.64) compared with other models with various statistical criteria. This result demonstrates the possibility of using the RFR model for heavy snowfall prediction. The proposed study can aid the government, local governments, and public institutions in developing strategies to respond to heavy snowfall in the fields of facilities, roads, and transportation. [ABSTRACT FROM AUTHOR]

This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using a nationwide multicenter dataset for Korean fetal growth. A total of 6549 term nulliparous women was included in the analysis, and the emergent CS rate was 16.1%. The C-statistics values for KNN, Voting, XGBoost, Stacking, gradient boosting, random forest, LGBM, logistic regression, and SVM were 0.6, 0.69, 0.64, 0.59, 0.66, 0.68, 0.68, 0.7, and 0.69, respectively. The logistic regression model showed the best predictive performance with an accuracy of 0.78. The machine learning model identified nine significant variables of maternal age, height, weight at pre-pregnancy, pregnancy-associated hypertension, gestational age, and fetal sonographic findings. The C-statistic value for the logistic regression machine learning model in the external validation set (1391 term nulliparous women) was 0.69, with an overall accuracy of 0.68, a specificity of 0.83, and a sensitivity of 0.41. Machine learning algorithms with clinical and sonographic parameters at near term could be useful tools to predict individual risk of emergent CS during active labor in nulliparous women. [ABSTRACT FROM AUTHOR]

We use 2011–2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24 h daily surface fine particulate matter (PM 2.5) concentrations at a continuous 6 × 6 km 2 resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data, including information encoded in GOCI AOD retrieval failure and trained with PM 2.5 observations from the three national networks. The predicted 24 h GOCI PM 2.5 concentrations for sites entirely withheld from training in a 10-fold cross-validation procedure correlate highly with network observations (R2 = 0.89) with a single-value precision of 26 %–32 %, depending on the country. Prediction of the annual mean values has R2 = 0.96 and a single-value precision of 12 %. GOCI PM 2.5 is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF and also because of RF smoothing of extreme PM 2.5 concentrations. The area-weighted and population-weighted trends of GOCI PM 2.5 concentrations for eastern China, South Korea, and Japan show steady 2015–2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of GOCI PM 2.5 fields for South Korea identifies hot spots where surface network sites were initially lacking and shows 2015–2019 PM 2.5 decreases across the country, except for flat concentrations in the Seoul metropolitan area. Inspection of the monthly PM 2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations in PM 2.5 , even though AOD and PM 2.5 often have opposite seasonalities. The application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality and spatial patterns on the 6 km scale. A comparison to a Community Multiscale Air Quality (CMAQ) simulation for the Korean peninsula demonstrates the value of the continuous GOCI PM 2.5 fields for testing air quality models, including over North Korea, where they offer a unique resource. [ABSTRACT FROM AUTHOR]

This paper presents two data-driven virtual sensors to estimate the time-series of the probability of high-concentration occurrence of naturally occurring radioactive materials (NORMs; 238U and 222Rn) in groundwater based on the in-situ groundwater quality monitoring data and geological information. The random forest was applied to estimate the NORM concentration based on the actual in-situ groundwater quality data, rock type, and the aquifer depth. Additionally, this study proposes three data sampling techniques (i.e., under-sampling, synthetic minority over-sampling, and a complex sampling) to improve the model applicability and accuracy. The developed models were validated using the actual data acquired from 201 locations in South Korea. The models for 238U and 222Rn showed estimation accuracies of 85% and 80%, respectively; the models with over-sampling showed better performance. All the results verified the usefulness of the developed models as virtual sensors for providing immediate information on the in-situ presence of NORMs in groundwater. [Display omitted] • Naturally occurring radioactive material (NORMs) in groundwater can be problematic. • Virtual sensor for in-situ estimating the probability of high-concentration of NORMs. • Major geochemical factors for NORMs were evaluated using statistical analyses. • Methods for solving data imbalance problem in training virtual sensor were proposed. • Major factors for the estimation were consistent with the statistical analysis result. [ABSTRACT FROM AUTHOR]

In recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area (A), mean drainage elevation (H), mean drainage slope (S), drainage density (D), runoff curve number ( C N ), watershed parameter ( L p ), and form factor ( R s ) etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport (MOLIT) was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method (SVM), the random forest method, and eXtreme Gradient Boosting (XGBoost). As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE 0.28, and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effectiveness of wireless emergency alerts (WEAs) on social distancing policy. The Republic of Korea has been providing information to the public through WEAs using mobile phones. This study used five data sets: WEA messages, news articles including the keyword "COVID-19," the number of confirmed COVID-19 patients, public foot traffic data, and the government's social distancing level. The WEAs were classified into two topics—"warning" and "guidance"—using a random forest model. The results of the correlation analysis and further detailed analysis confirmed that the "warning" WEA topic and number of news articles significantly affected public foot traffic. However, the "guidance" topic was not significantly associated with public foot traffic. In general, the Korean government's WEAs were effective at encouraging the public to follow social distance recommendations during the COVID-19 pandemic. In particular, the "warning" WEA topic, by providing information about the relative risk directly concerning the recipients, was significantly more effective than the "guidance" topic. [ABSTRACT FROM AUTHOR]

The purpose of this study was to apply the random forest (RF), XGBoost, and LightGBM machine learning (ML) algorithms to land cover classification, and to present the model tuning process for each algorithm. Sentinel-2 satellite images were used for land cover classification, and the land cover map provided by the Ministry of Environment of the Republic of Korea was used as label data. Each ML algorithm was applied using the constructed dataset. In addition, each ML algorithm was optimized by three methods (grid search, random search, and Bayesian optimization). The grid search took the longest time to optimize the hyperparameters because it required the highest number of search iterations, but the accuracy was highest. The random search was the fastest method of optimizing the hyperparameters. The accuracy of XGBoost was the highest for each ML algorithm. The prediction of XGBoost was the most consistent with the land cover map provided by the Ministry of Environment. However, the LightGBM algorithm has a major advantage in terms of the algorithm optimization and application time. Therefore, our study is meaningful in that we obtained a higher accuracy and shorter time for each ML algorithm. [ABSTRACT FROM AUTHOR]

We use 2011-2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6x6 km² resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data and trained with PM2.5 observations from the three national networks. The predicted 24-h PM2.5 concentrations for sites entirely withheld from training in a ten-fold crossvalidation procedure correlate highly with network observations (R² = 0.89) with single-value precision of 26-32% depending on country. Prediction of annual mean values has R² = 0.96 and single-value precision of 12%. The RF algorithm is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF, and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of RF PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015-2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of RF PM2.5 fields for South Korea identifies hotspots where surface network sites were initially lacking and shows 2015-2019 PM2.5 decreases across the country except for flat concentrations in the Seoul metropolitan area. Inspection of monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations of PM2.5 even though AOD and PM2.5 often have opposite seasonalities. Application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality as well as spatial patterns on the 6 km scale. Comparison to a CMAQ simulation for the Korean peninsula demonstrates the value of the continuous RF PM2.5 fields for testing air quality models, including over North Korea where they offer a unique resource. [ABSTRACT FROM AUTHOR]

Background: Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes. Methods: A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models—logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost—and compared the prediction performances thereof. Results: Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes. Conclusion: An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate and compare the performances of 5 machine learning (ML) techniques for predicting the spatial probability of landslide at Atsuma, Japan, and Mt. Umyeon, Korea. 5 ML models used are Naïve Bayes (NB), ensemble learning (random forest (RF) and adaboost (AB)), and deep learning (multilayer perceptron (MLP) and convolutional neural network (CNN)) models. Real landslide events at the study areas are randomly separated to the training set for landslide mapping and the validation set for assessing performance. To assess the performance of the used models, the resulting models are validated using receiver operating characteristic (ROC) curve. The success rate curves show that the areas under the curve (AUC) for the NB, RF, AB, MLP, and CNN are 85.1, 88.8, 88.6, 87.5, and 95.0%, respectively, at Atsuma and 68.7, 85.6, 90.5, 81.6, and 92.0%, respectively, at Mt. Umyeon. Similarly, the validation results show that the areas under the curve for the NB, RF, AB, MLP, and CNN are 84.3, 87.1, 87.1, 86.7, and 89.7%, respectively, at Atsuma and 64.9, 85.5, 83.9, 84.7, and 90.5%, respectively, at Mt. Umyeon. In addition, statistical tests such as Chi-square test and difference of proportions test show that all classified landslide susceptibility maps have statistical significance and the significant difference in classified landslide susceptibility maps from different ML models. The comparison results among 5 ML models show that the CNN model had the best performance and NB model had the worst performance in both study areas. [ABSTRACT FROM AUTHOR]

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process. [ABSTRACT FROM AUTHOR]

[Display omitted] • Developed the AquaShan Index, an advanced method for river health assessment combining biological and environmental data. • Applied Random Forest analysis for precise weighting of environmental variables in river ecosystem evaluation. • Analyzed extensive data from South Korea's river ecosystems, focusing on benthic macroinvertebrates and environmental factors. • Demonstrated greater accuracy of the AquaShan Index in identifying river health issues compared to traditional indices. • Provided a practical, adaptable tool for informed river management and conservation decision-making. This study introduces the AquaShan Index, an innovative approach to assessing river health, which integrates biological indicators with key environmental variables. This index represents an advancement over traditional methods, like the Shannon Diversity Index, by including both species diversity and environmental factors such as dissolved oxygen, pH, and water temperature. Utilizing data from the Stream/River Ecosystem Survey and Health Assessment in South Korea, covering 5,952 data over three years, the study combines species data of benthic macroinvertebrates with environmental parameters, applying Random Forest analysis to weight these variables. The AquaShan Index formula incorporates species' pollution tolerance scores, offering a nuanced perspective on ecosystem resilience to environmental stressors. Our results show a wide range of river health conditions, with the AquaShan Index demonstrating greater sensitivity and specificity in identifying ecological impairments compared to traditional indices. The inclusion of pollution tolerance scores and environmental variables provides a more comprehensive assessment of river health, revealing a higher number of sites with ecological challenges than previously identified. The AquaShan Index's adaptability and comprehensive approach make it a valuable tool for informed decision-making in river conservation and management, offering an enhanced methodology for assessing and sustaining river ecosystem health. [ABSTRACT FROM AUTHOR]

PM 10 and SO 2 are major air pollutants in Ulsan, the largest industrial city in South Korea. The purpose of this study was to evaluate the effectiveness of pollution reduction policies by comparing the pollution characteristics of PM 10 and SO 2 between 2012 and 2020. The concentrations of PM 10 and SO 2 have substantially decreased over time. Local industry was the primary driver of SO 2 levels, whereas various sources were associated with PM 10 levels. In 2012 and 2020, the spatial distributions of SO 2 were similar, with hot spots around petrochemical and nonferrous industrial complexes, whereas the sources of PM 10 differed between the two years. Although industrial emissions of PM 10 decreased from 2012 to 2020, the concentrations derived from an air dispersion model were slightly higher in 2020 due to changes in both wind patterns and the strength of the emission sources. In contrast, modeled SO 2 levels were higher in 2012 than in 2020 due to greater emissions in 2012. Eastern and northeastern China were identified as potential source areas for the long-range atmospheric transport of both PM 10 and SO 2 , though its influence was significantly lower in 2020. A comparison of the PM 10 and SO 2 concentrations predicted by a random forest model and the measured concentrations indicates that government policies have been effective in reducing the emissions and ambient air concentrations of both pollutants. In conclusion, this study demonstrates that local industry remains a significant contributor to air pollution in Ulsan despite the effects of governmental policy. Furthermore, as the influence of external sources weakens, there is a pressing need to strengthen the management of local sources to effectively mitigate air pollution in Ulsan. [Display omitted] • The effectiveness of pollution reduction policies for PM 10 and SO 2 was evaluated. • The influence of LRAT and local sources has significantly decreased recently. • Governmental policies have led to a decrease in PM 10 and SO 2 concentrations. • However, local industry remains a significant contributor to air pollution. • The result of the study can be used to develop industry-specific management policies. • To 5 bullet points (maximum 85 characters, including spaces, per bullet point). [ABSTRACT FROM AUTHOR]

• A two-stage data mining method is used for identifying factors affecting the safety of taxi drivers. • Random forest analyses are adopted for the data mining. • An in-depth questionnaire survey is conducted to obtain intrinsic characteristics underlying the cause of taxi crashes. • A set of policy implications is established for enhancing taxi safety. Introduction: Traffic safety issues associated with taxis are important because the frequency of taxi crashes is significantly higher than that of other vehicle types. The purpose of this study is to derive safety implications to be used for developing policies to enhance taxi safety based on analyzing intrinsic characteristics underlying the cause of traffic accidents. Method: An in-depth questionnaire survey was conducted to collect a set of useful data representing the intrinsic characteristics. A total of 781 corporate taxi drivers participated in the survey in Korea. The proposed analysis methodology consists of two-stage data mining techniques, including a random forest method, with data that represents the working condition and welfare environment of taxi drivers. In the first stage, the drivers' intrinsic characteristics were derived to classify four types of taxi drivers: unspecified normal, work-life balanced, overstressed, and work-oriented. Next, priority was determined for classifying high-risk taxi drivers based on factors derived from the first analysis. Results: The derived policies can be categorized into three groups: 'the development of new policies,' 'the improvement of existing policies,' and 'the elimination of negative factors.' Establishing a driving capability evaluation system for elderly drivers, developing mental health management programs for taxi drivers, and inspecting the taxi's internal conditions were proposed as new policies. Improving the driver's wage system, supporting the improvement of rest facilities, and supporting the installation of security devices for protecting taxi drivers are methods for improving existing policies to reinforce the traffic safety of taxi drivers. Last, restricting overtime work for taxi drivers was proposed as a policy to eliminate negative factors for improving taxi traffic safety. Practical Applications: It is expected that by devising effective policies using the policy implications suggested in this study, taxi traffic accidents can be prevented and the quality of life of taxi drivers can be improved. [ABSTRACT FROM AUTHOR]

Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015–2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (R2). In a comparative analysis with observed values, the proposed model showed an R2 value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable. [ABSTRACT FROM AUTHOR]

Background: The recent pandemic of CoVID-19 has emerged as a threat to global health security. There are very few prognostic models on CoVID-19 using machine learning. Objectives: To predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. Materials and Methods: Mortality for confirmed CoVID-19 patients (n = 3,524) between January 20, 2020 and May 30, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). The performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool. Results: The logistic regression algorithm was the best performer in terms of discrimination (area under ROC curve = 0.830), calibration (Matthews Correlation Coefficient = 0.433; Brier Score = 0.036) and. The best performing algorithm (logistic regression) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/). Conclusions: We describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policymakers to triage patients at the community level in addition to other approaches. [ABSTRACT FROM AUTHOR]

Mass appraisal is the standardized procedure of valuing a large number of properties at the same time and is commonly used to compute real estate tax. While a hedonic pricing model based on the ordinary least squares (OLS) linear regression has been employed as the traditional method in this process, the stability and accuracy of the model remain questionable. This paper investigates the features of a house price predictor based on the Random Forest (RF) method by comparing it with that of a conventional hedonic pricing model. We used apartment transaction data from the period of 2006 to 2017 in the district of Gangnam, one of the most developed areas in South Korea. Using a data set covering 40% of all transactions in the sample area, we demonstrate that the accuracy of a machine learning-based predictor can be surprisingly high. The average of percentage deviations between the predicted and the actual market price was found to be only around 5.5% in the RF predictor, whereas it was almost 20% in the OLS-based predictor. With the RF predictor, the probability of the predicted price being within 5% of its actual market price was 72%, while only about 17.5% of the regression-based predictions fell within the same range. These results show that, in the practice of mass appraisal, the RF method may be a useful complement to the hedonic models, as it more adequately captures the complexity or non-linearity of actual housing markets. [ABSTRACT FROM AUTHOR]