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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]

High-growth firms (HGFs), also known as gazelle firms, have attracted considerable attention due to their outstanding contributions to job creation and additional spillover benefits. Despite years of work, it is still challenging to predict high-growth firms, and many essential HGF characteristics are yet unknown. This study analyzes high-quality Korean firm data and predicts high-growth firms using big data and machine learning methods, such as LASSO, adaptive LASSO, and random forest analysis. Besides, we include variables related to business strategies and activities such as innovation, information systems like ERP, outsourcing, strategic alliances, and expansion to international markets to determine whether the addition of this information enhances HGF prediction. We discovered the significance of revenue growth, efficiency management, asset investment, and human resource management skills in increasing one's chances of becoming an HGF. The findings suggest that rather than being an erratic process or being purely driven by structural characteristics, firm growth is determined by the firm's endogenous strategic characteristics. Plain English Summary: With cutting-edge machine learning techniques including LASSO, adaptive LASSO, and random forest, we discovered the significance of revenue growth, efficiency management, asset investment, and human resource management skills in increasing one's chances of becoming an HGF. [ABSTRACT FROM AUTHOR]

Falls among older people are a major health concern. This study aims to develop a multifactorial fall risk assessment system for older people using a low-cost, markerless Microsoft Kinect. A Kinect-based test battery was designed to comprehensively assess major fall risk factors. A follow-up experiment was conducted with 102 older participants to assess their fall risks. Participants were divided into high and low fall risk groups based on their prospective falls over a 6-month period. Results showed that the high fall risk group performed significantly worse on the Kinect-based test battery. The developed random forest classification model achieved an average classification accuracy of 84.7%. In addition, the individual's performance was computed as the percentile value of a normative database to visualise deficiencies and targets for intervention. These findings indicate that the developed system can not only screen out 'at risk' older individuals with good accuracy, but also identify potential fall risk factors for effective fall intervention. Practitioner summary: Falls are the leading cause of injuries in older people. We newly developed a multifactorial fall risk assessment system for older people utilising a low-cost, markerless Kinect. Results showed that the developed system can screen out 'at risk' individuals and identify potential risk factors for effective fall intervention. [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]

According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study, to detect malicious comments, we used approximately 9,400 examples of hate speech from a Korean corpus of entertainment news comments. We developed toxic comment classification models using supervised learning algorithms, including decision trees, random forest, a support vector machine, and K-nearest neighbors. The proposed model uses random forests to classify toxic words, achieving an F1-score of 0.94. We analyzed the trained model using the permutation feature importance, which is an explanatory machine learning method. Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea. Using this research methodology, the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean. [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]

Recently, various corporate failure prediction models that use machine learning techniques have received considerable attention. In particular, using a sequence of a company's historical information, rather than just the most recent information, yields better predictive performance by adopting recurrent neural networks (RNNs) and long short-term memory (LSTM) algorithms in the United States market. Similarly, we evaluate whether these results hold in emerging market contexts using listed companies in Korea. We also compare the logistic regression, random forest, RNN, LSTM, and an ensemble model combining these four techniques. The random forest model with recent information outperforms the other models, indicating that corporate failure prediction models for immature markets, unlike those for developed markets, might have to focus more on recent information rather than on the historical sequence of corporate performance. [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]

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]

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]

Highly pathogenic avian influenza (HPAI) is a fatal zoonotic disease that damages the poultry industry and endangers human lives via exposure to the pathogen. A risk assessment model that precisely predicts high‐risk groups and occurrence of HPAI infection is essential for effective biosecurity measures that minimize the socio‐economic losses of massive outbreaks. However, the conventional risk prediction approaches have difficulty incorporating the broad range of factors associated with HPAI infections at poultry holdings. Therefore, it is difficult to accommodate the complexity of the dynamic transmission mechanisms and generate risk estimation on a real‐time basis. We proposed a continuous risk prediction framework for HPAI occurrences that used machine learning algorithms (MLAs). This integrated environmental, on‐farm biosecurity, meteorological, vehicle movement tracking, and HPAI wild bird surveillance data to improve accuracy and timeliness. This framework consisted of (i) the generation of 1788 predictors from six types of data and reconstructed them with an outcome variable into a data mart based on a temporal assumption (i.e. infected period and day‐ahead forecasting); (ii) training of the predictors with the temporally rearranged outcome variable that corresponded to HPAI H5N6 infected state at each individual farm on daily basis during the 2016–2017 HPAI epidemic using three different MLAs [Random Forest, Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting]; (iii) predicting the daily risk of HPAI infection during the 2017–2018 HPAI epidemic using the pre‐trained MLA models for each farm across the country. The models predicted the high risk to 8–10 out of 19 infected premises during the infected period in advance. The GBM MLAs outperformed the 7‐day forecasting of HPAI prediction at individual poultry holdings, with an area under the curve (AUC) of receiver operating characteristic of 0.88. Therefore, this approach enhances the flexibility and timing of interventions against HPAI outbreaks at poultry farms. [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]

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]

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]

Climate change increases both the risks and effects of flooding in urban areas, which, without mitigation, may lead to social catastrophes. In Korea, devastating typhoons and overflows account for approximately 90% of the country's natural disasters, and the many man-made features of urban environments exacerbate the detrimental effects whenever flooding occurs. Many regression analysis methods exist for assessing geographical flood risk; furthermore, a handful of machine learning methods have been created for mitigation and estimation purposes—there are none for prevention. Therefore, in this study, we developed a machine learning flood assessment model that leverages several machine learning models for the Busan Metropolitan City. Each was applied to a test dataset, and their performances were evaluated based on accuracy, sensitivity, specificity, and area under the curve; thereafter, the model determined to be the most reliable was used to create a flood risk assessment map. The model was then used to assess the areas of highest probability of flooding. Upon its completion, we discovered that flooding may now occur with less rainfall than that of the 10-year return period. The derived map is expected to be used as a basic source for the development of preventive countermeasures against urban flooding, thus contributing to the enhancement of flood control and response capacities in applicable regions. [ABSTRACT FROM AUTHOR]

• 23.28% of Korean adolescents were overweight or obese. • Nine machine learning-based models achieved accuracy of 0.7662 to 0.8403. • The study analyzed feature importance via machine learning methods. • Machine learning identifies a total of 22 factors behind Korean teen obesity. • Our study underscores the vital need for tailored and collective prevention programs. Overweight and obesity in adolescents have been reported as one of the most serious threats worldwide including South Korea. This study aims to investigate the complex factors contributing to overweight and obesity in Korean adolescents using various machine learning methods. The research includes a dataset of 43,268 records from the 16th Korean Youth Risk Behavior Web-based Survey and explores 71 different factors, such as sociodemographic characteristics, dietary habits, health, behavior problems, family, and peer and school-related factors. Our analysis encompassed an array of algorithms, including Logistic Regression, Ridge, LASSO, Elasticnet, Decision tree, Bagging, Random forest, AdaBoost, and XGBoost. A total of nine machine learning models exhibited accuracy levels within the range of 0.7662 to 0.8403. Based on the domains and sub-domains of factors, it was determined that domains including sociodemographic characteristics, dietary habits, physical health, psychological health, behavioral problems, family factor, and peer and school factors were deemed important. Additionally, it is suggested that attention be given to newly-emerged features indicated by machine learning techniques, including oral health, smartphone addiction, smoking, sexual behavior, school violence, and nationality of parents. The current study's findings emphasize the critical need for collective and customized prevention programs considering multi-facet features to prevent overweight and obesity among Korean adolescents. [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]

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]

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]

• We conduct data-driven approaches to distinguishing political orientations and news outlets in South Korea. • Three machine learning techniques with the ensemble methods are employed. • With about 51,000 national defense-related news articles, 91.9% (political orientations) and 84.0% (news outlets) are achieved. With the advancement of the internet, the public now has easy access to news from various media outlets. However, a number of news outlets tend to report their content based on their political orientations or affiliations, which may compromise the objectivity of the news. This research used machine learning to analyze whether it is possible to tell the political orientation and news outlet apart based on news articles in South Korea. We collected a lot of news articles spanning over five years and used the text data. We chose major conservative and progressive news outlets and tried classifying them into two groups. We even looked into classifying articles by each news outlet. We used different machine learning methods like Logistic Regression, Random Forest Classifier, and eXtreme Gradient Boosting, and tried to improve the performance by combining these models. The research found that the combined model had high accuracy, up to 91.9% for binary classification of news outlet political orientations and up to 84.0% for classifying news outlets in multiple categories. This shows that you can determine the political leaning of news outlets based on their articles, highlighting the importance of considering bias in news outlets when evaluating information instead of solely relying on the article content. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) was applied to model tree mortality and growth reduction. • Biogeochemical model produced ecophysiological variables for the ML modeling. • Random forest (RF) showed the best performance among the MLs. • Sugar and starch contents were identified as key parameters in the ML models. Massive tree diebacks and abnormal growth reduction have been more reported across continents, which calls an attention for methods that can assess the physiological stress of tree growth. This study aims to develop machine learning models (MLM) to explain tree mortality and growth reduction in terms of the key parameters of climate and tree ecophysiology in temperate forests of Korea. For this, we produced various ecophysiological parameters with a process-based vegetation model that was used as inputs to multiple machine learning algorithms and the conventional multiple linear regression (MLR). As a result, the MLMs outperformed the MLR. Among the MLMs, random forest (RF) showed the highest overall accuracy and AUC for both tree mortality (79% and 0.84±0.09, respectively) and growth reduction (61% and 0.63 ± 0.09). Winter temperature along with previous-year autumn soluble sugar content and precipitation were the most important in determining the tree mortality, while the growth reduction was largely regulated by current-year conditions such spring and autumn precipitation and summer starch content. Also, in the proportion of variance (communalities) of PCA, the precipitation and soluble sugar showed low values 0.15 and 0.29, respectively, which low communalities (<0.3) indicate that these variables have limited shared variance with other variables in the analysis. This study indicates the combined use of vegetation model and MLM can give reciprocal benefits to each other by providing ecophysiological parameters for MLM, otherwise hard to get, and coping with convoluted process of tree vitality undescribed yet in the vegetation model, respectively. [ABSTRACT FROM AUTHOR]

Market capitalization of firms provides valuable information for analyzing stock markets and the size factor is widely used in factor-based investing. Some markets, such as the Korean market, are especially interesting in this respect because they contain extremely large public firms. This study analyzes the effect of the largest firm on factor investing through machine learning models that are effective for variable selection. We demonstrate how machine learning can be used for identifying important factors. Our comparison between US and Korean markets shows the significance of separating the largest firm in analyzing how factors impact performance in the Korean market. • Analyze the Korean stock market, which contains one exceptionally large firm • Compare the effects of the largest public firm between the US and Korea • Use machine learning methods to perform variable selection that includes the effects of the largest firm • Lasso regression and random forest provide feature selection and variable importance for analyzing of market factors • Empirical results reveal unique characteristics of the Korean stock market [ABSTRACT FROM AUTHOR]

This study developed a model to predict concentrations of chlorophyll -a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models, then analyzed the relationship between [Chl-a] and the input variables of the optimized model. Daily water quality and meteorological data from 2012 to 2020 were collected from the real-time water quality information system and the meteorological administration of Korea. To quantify model accuracy, the coefficient of determination, root mean square error, and mean absolute error were applied. Among random forest (RF), support vector machine, and artificial neural network, the RF with random dataset showed the highest accuracy. The RF was optimized when 78 trees were applied to the model. Input variables for the best RF model were total organic carbon (feature importance: 27%), total nitrogen (19%), pH (13%), water temperature (8%), total phosphorus (8%), electrical conductivity (7%), dissolved oxygen (6%), minimum air temperature (AT) (4%), mean AT (3%), and maximum AT (3%). The feature-importance analysis showed that total organic carbon was the most important variable to predict [Chl-a] in the Han river basin. Total nitrogen was a more important variable than total phosphorus. [Display omitted] • The optimal input variables were selected through Pearson's correlation analysis. • Random forest (RF) was most suitable for predicting chlorophyll -a ([Chl-a]). • RF had 10 input variables including total organic carbon, nitrogen, and phosphorus. • Total nitrogen was more important than total phosphorus in predicting [Chl-a]. • RF can manage algae associated with multiple monitoring stations in river basin. [ABSTRACT FROM AUTHOR]

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years. [ABSTRACT FROM AUTHOR]

Recent cases of climate disasters such as the European floods in 2021 and Korea's longest rainy season in 2020 strongly imply the importance of adaptation to climate change. In this study, we performed a numerical prediction on how much climate change adaptation factors related to forest policy can reduce climate disasters such as landslides. We focused on the landslide in Korea and applied a machine learning model reflecting adaptive indicators in the representative concentration pathway 8.5 climate scenario. The changes in the landslide probability were estimated using the Random Forest model, which estimated the landslide probability in the baseline period (2011) with excellent performance, and the spatial adaptation indicators used in this study contributed approximately 20%. The future landslide risk predicting indicated a significant increase in the Very High and High risk areas, especially in 2092. The application of the forest-related adaptation indices based on the policy scenario showed that in 2050, the effect was not pronounced, but in 2092, when the risk of landslides was much higher, the effect increased significantly. In particular, the effect was remarkable in the Seoul metropolitan and southern coastal regions. Even with the same adaptive capacity, it exerted a larger effect on the enhanced disasters. Our results suggest that the enhancement of adaptive capacity can reduce landslide risk up to 70% in a Very High risk region. In conclusion, it implies an importance to respond to the intensifying climate disasters, and abundant follow-up studies are expected to appear in the future. [ABSTRACT FROM AUTHOR]

• Harmonization and quality control of originally heterogenous observational databases. • Large-scale application of machine learning methods in a distributed data network. • Transparent use of open-source software tools and publicly shared analytical code. As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g. , by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is one of the most destructive diseases affecting pine trees, and South Korea is one of the most severely damaged countries in the world. Based on the information on PWD occurrence and their environmental characteristics (i.e., geographical, meteorological, and land-use) in South Korea, we evaluated the conditions most conducive for PWD occurrence and developed projection models using machine learning algorithms; random forest (RF) and maximum entropy (Maxent). Our results showed that PWD mainly occurred in areas like highly urbanized area; low elevations, at close proximity to roads. Also, both RF and Maxent models presented high prediction performance for PWD occurrence. Geographical factors (e.g., elevation and distance to roads) were major determinants of PWD occurrence and largely contributed to explaining variability and partial dependence plots of each model. We developed an ensemble model composed of the RF and Maxent models to predict a potential risk map for PWD occurrence on a national scale. In South Korea, most territory was included potential risk of PWD occurrence, and it was predicted to be expanded in the future according to the climate change. The study results showed a high utility for use in surveillance and monitoring of PWD occurrence by inferring the spread pathway or spread direction of PWD based on the potential occurrence map. [Display omitted] • An ensemble model with random forest and maximum entropy model was developed for the prediction of PWD occurrence. • The ensemble model as well as each individuals model presented high prediction performance for PWD occurrence. • The potential distribution of PWD was evaluated under current and future climate condition. • The potential distribution of PWD was predicted to be expanded in the future according to the climate change. • The risk map of PWD from ensemble model has a high potentiality for surveillance and monitoring of PWD occurrence. [ABSTRACT FROM AUTHOR]