• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.663-673
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• 2011

In the face of accelerating biodiversity loss and its significance in our coexistence with nature, biodiversity is becoming more crucial in sustainable development perspective. To estimate biodiversity in the future which provides valuable information for decision making system especially in the national level, a quantitative approach must be studied forehand as a baseline of the present status. In this study, we developed a large-scale map of Plant Species Richness (PSR, typical indicator of biodiversity) for Young-dong and Pyung-chang provinces. Due to the accessibility of appropriate data and advance of modelling techniques, reduction of variables without deteriorating the predictive power is considered by applying Genetic algorithm. In addition, a number of Correctly Classified Instances (CCI) with 10-fold cross validation which indicates the predictive power, was carried out for evaluation. This study, as a fundamental baseline, will be beneficial in future land work as well as ecosystem restoration business or other relevant decision making agenda.

• Transactions of Materials Processing
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• v.26 no.6
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• pp.341-347
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• 2017

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to produce multi-curvature surfaces by controlling strain distribution along longitudinal direction. Reconfigurable rollers could be arranged to implement a kind of punch die set. By utilizing these reconfigurable rollers, desired curved surface can be formed. In FRRF process, three-dimensional surface is formed from two-dimensional curve. Thus, it is difficult to predict the forming result. In this study, a regression analysis was suggested to construct a predictive model for a longitudinal curvature of FRRF process. To facilitate investigation, input parameters affecting the longitudinal curvature of FRRF were determined as maximum compression value, curvature radius in the transverse direction, and initial blank width. Three-factor three-level full factorial experimental design was utilized and 27 experiments using FRRF apparatus were performed to obtain sample data of the regression model. Regression analysis was carried out using experimental results as sample data. The model used for regression analysis was a quadratic nonlinear regression model. Determination factor and root mean square root error were calculated to confirm the conformity of this model. Through goodness of fit test, this regression predictive model was verified.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.3
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• pp.93-102
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• 2016

An invasive disturbance species has caused harm to biodiversity and ecosystem. To address the issue, identifying the characteristics of a habitat for invasive disturbance species is considered for forest management. This study analyzed a status of plant species by field survey based on belt transect method in the capital areas and established a predictive model for invasive disturbance species by logistic regression. As results of the study, the number of herb, vine, and invasive disturbance species and a canopy cover of tree would decrease from the forest edge to core areas (p<0.001). The predictive model was derived with variables of altitude, Topographic Wetness Index, distance to forest edge, and canopy cover of tree. It can be useful in estimating the presence or absence of species and predicting its spatial distribution. Further studies are needed to identify the pathway of introduction, spread, and possibility of germination for understanding the status of invasive disturbance species in more depth.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.872-878
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• 2006

In recent years the importance of the preservation of cultural artefacts like ancient tombs has been widely accepted domestically and internationally as the quality of life improves. However not much technical attention has been paid for the facilities and systems to preserve those artefacts. Even the general understanding of the preservatory environment of the underground space as tombs is poor. As a part of the present study, the temperature and relative humidity inside a selected artefact, Shinkwan-ri tomb, have been monitored for a year round by the present author to improve the understanding of the indoor thermal environment, is pursued to provide a predictive tool of numerical modelling of Shinkwan-ri tomb the opened underground space thermal environment. In this study, predictive numerical modelling of Shinkwan-ri tomb using the Computational Fluid Dynamics, calculate the velocity and temperature distribution and offer basic data which are necessary for the best fitted design of tomb air-conditioning device.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.442-452
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• 2022

This study was conducted to understand research trends of HR Analytics (HRA) in Korea and to suggest future research directions. First, a comparative analysis was conducted by classifying six areas of recruitment on-board, work environment, performance evaluation, retention, and exit/retirement building on the employee life cycle framework. The results indicate that first, the distribution of detailed research topics in Korean HRA research has similar to that of international research. Second, Korean HRA studies related to employee training and development function are insufficient. Third, the scope and the method of machine learning are becoming enriched. Finally Korean HRA studies are still in the technical domain and toward entering the predictive analysis domain.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of Environmental Impact Assessment
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• v.21 no.4
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• pp.593-607
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• 2012

The Ministry of Environment have started the 'National Ecosystem Survey' since 1986. It has been carried out nationwide every ten years as the largest survey project in Korea. The second one and the third one produced the GIS-based inventory of species. Three survey methods were different from each other. There were few studies for species distribution using national survey data in Korea. The purposes of this study are to test species distribution models for finding the most suitable modeling methods for the National Ecosystem Survey data and to investigate the modeling results according to survey methods and taxonominal group. Occurrence data of nine species were extracted from the National Ecosystem Survey by taxonomical group (plant, mammal, and bird). Plants are Korean winter hazel (Corylopsis coreana), Iris odaesanensis (Iris odaesanensis), and Berchemia (Berchemia berchemiaefolia). Mammals are Korean Goral (Nemorhaedus goral), Marten (Martes flavigula koreana), and Leopard cat (Felis bengalensis). Birds are Black Woodpecker (Dryocopus martius), Eagle Owl (Bubo Bubo), and Common Buzzard (Buteo buteo). Environmental variables consisted of climate, topography, soil and vegetation structure. Two modeling methods (GAM, Maxent) were tested across nine species, and predictive species maps of target species were produced. The results of this study were as follows. Firstly, Maxent showed similar 5 cross-validated AUC with GAM. Maxent is more useful model to develop than GAM because National Ecosystem Survey data has presence-only data. Therefore, Maxent is more useful species distribution model for National Ecosystem Survey data. Secondly, the modeling results between the second and third survey methods showed sometimes different because of each different surveying methods. Therefore, we need to combine two data for producing a reasonable result. Lastly, modeling result showed different predicted distribution pattern by taxonominal group. These results should be considered if we want to develop a species distribution model using the National Ecosystem Survey and apply it to a nationwide biodiversity research.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.427-437
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• 2019

Background: Climate change is believed to be continuously affecting ticks by influencing their habitat suitability. However, we attempted to model the climate change-induced impacts on future genus Rhipicephalus distribution considering the major environmental factors that would influence the tick. Therefore, 50 tick occuance points were taken to model the potential distribution using maximum entropy (MaxEnt) software and 19 climatic variables, taking into account the ability for future climatic change under representative concentration pathways (RCPs) 4.5 and 8.5, were used. Results: MaxEnt model performance was tested and found with the AUC value of 0.99 which indicates excellent goodness-of-fit and predictive accuracy. Current models predict increased temperatures, both in the mid and end terms together with possible changes of other climatic factors like precipitation which may lead to higher tick-borne disease risks associated with expansion of the range of the targeted tick distribution. Distribution maps were constructed for the current, 2050, and 2070 for the two greenhouse gas scenarios and the most dramatic scenario; RCP 8.5 produced the highest increase probable distribution range. Conclusions: The future potential distribution of the genus Rhipicephalus show potential expansion to the new areas due to the future climatic suitability increase. These results indicate that the genus population of the targeted tick could emerge in areas in which they are currently lacking; increased incidence of tick-borne diseases poses further risk which can affect cattle production and productivity, thereby affecting the livelihood of smallholding farmers. Therefore, it is recommended to implement climate change adaptation practices to minimize the impacts.