• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.207-218
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• 2010

Generalized additive model(GAM) is the statistical model that resolves most of the problems existing in the traditional linear regression model. However, overfitting phenomenon can be aroused without applying any method to reduce the number of independent variables. Therefore, variable selection methods in generalized additive model are needed. Recently, Lasso related methods are popular for variable selection in regression analysis. In this research, we consider Group Lasso and Elastic net models for variable selection in GAM and propose an algorithm for finding solutions. We compare the proposed methods via Monte Carlo simulation and applying auto insurance data in the fiscal year 2005. lt is shown that the proposed methods result in the better performance.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.59-64
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• 2008

We need to use the strength of each Species Distribution Model(SDM) because presence location data were only collected due to time and economic limitations in Korea. This study investigated and compared GAM(Generalized Additive Model) which is one of presence-absence models with Maxent(Maximum Entropy Model) which is one of presence only models according to location data(presence/absence data). The target species was Fisher(Martes pennanti) which is an endangered species in California, USA. We implemented environmental data such as topography, climate and vegetation, and applied models to sub-regions and study area. The results of this study were as follows. Firstly, GAM which used real presence and absence data was better than GAM which used pseudo-absence data and Maxent which used presence-only data. Secondly, Maxent was better than GAM when presence-only data were used. Lastly, each model which applied to different regions didn't predict other area well due to the difference of habitat environment and over-predicted outside of study area. We need to select an optimal model to predict a suitable habitat according to the type and distribution of location data.

• Fisheries and Aquatic Sciences
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• v.23 no.7
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• pp.19.1-19.14
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• 2020

Three generalized additive models were applied to the distribution of anchovy eggs and oceanographic factors to determine the occurrence of anchovy spawning grounds in Korean waters and to identify the indicators of their occurrence using survey data from the spring and summer of 1985, 1995, and 2002. Binomial and Gaussian types of generalized additive models (GAM) and quantile generalized additive models (QGAM) revealed that egg density was influenced mostly by ocean temperature and salinity in spring, and the vertical structure of temperature, salinity, dissolved oxygen, and zooplankton biomass during summer in the upper quantiles of egg density. The GAM and QGAM model deviance explained 18.5-63.2% of the egg distribution in summer in the East and West Sea. For the principle component analysis-based GAMs, the variance explained by the final regression model was 27.3-67.0%, higher than the regular models and QGAMs for egg density in the East and West Sea. By analyzing the distribution of anchovy eggs off the Korean coast, our results revealed the optimal temperature and salinity conditions, in addition to high production and high vertical mixing, as the key indicators of the major spawning grounds of anchovies.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.19-43
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• 2011

Tuning the architecture of SVM (support vector machine) is to build an SVM model of better performance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper suggests a combined method for tuning the architecture of SVM models, which employs the GAM (generalized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.11-18
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• 2012

Increasing concern about biodiversity has lead to a rise in demand on the spatial assessment of biological resources such as biodiversity assessment, protected area selection, habitat management and restoration in Korea. The purpose of this study is to create species richness map through data collection and modeling techniques for wildlife habitat assessment. The GAM (Generalized Additive Model) is easy to interpret and shows better relationship between environmental variables and a response variable than an existing overlap analysis and GLM (Generalized Linear Model). The study area delineated by a large watershed contains Jirisan national park, Mt. Baekun and Sumjin river with three kinds of protected areas (a national park, a landscape ecology protected area and an otter protected area). We collected the presence-absence data for wildlife (mammals and birds) using a stratified random sampling based on a land cover in the study area and implemented natural and socio-environmental data affecting wildlife habitats. After doing a habitat use analysis and specifying significant factors for each species, we built habitat suitability models using a presence-absence model and created habitat suitability maps for each species. Biodiversity maps were generated by taxa and all species using habitat suitability maps. Significant factors affecting each species habitat were different according to their habitat selection. Although some species like a water deer or a great tit were distributed at the low elevation, most potential habitats for mammals and birds were found at the edge of a national park boundary or near a forest around the medium elevation of a mountain range. This study will be used for a basis on biodiversity assessment and proected area selection carried out by Ministry of Environment.

• Korean Journal of Health Education and Promotion
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• v.25 no.1
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• pp.13-23
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• 2008

Objective: The current study was to test if the developed sphygmomanometer was working well and blood pressure information could be collected and monitored systematically through the internet. We tested if the sphygmomanometer and services for blood pressure controlled high blood pressure significantly and the ubiquitous monitoring could be used further. Methods: Kyungwon University, KT Co., Gil Medical Center, LIG Nex1 Co., and Sujeong Health Center conducted an ubiquitous high blood control project in Sujeong-gu, Sungnam, Korea from Mar. 5 to May 16. We developed and applied sphygmomanometer. We distributed the devices to 27 high blood pressure patients. The blood pressures of the residents were monitored through the internet when they measured blood pressures in their homes. A nurse monitored and consulted their blood pressures in the monitoring center in Kyungwon University during the demonstration period. The consultant called them and consulted on their blood pressures in few seconds they used the sphygmomanometers. For the significance of change in blood pressure, we tested statistically with Generalized Additive Model(GAM) and Multi-level Analysis. Results: Both GAM and Multi-level Analysis showed that the blood pressures of persons with ubiquitous blood pressure management decreased significantly as time passed. Conclusions: The internet monitoring and services are considered to be promising because most of the participants were satisfied especially because somebody was caring their health. The decrease of blood pressures was significant by GAM and Multi-level Analysis. Thus, we can apply ubiquitous blood pressure management to health promotion projects.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.201-217
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• 2016

We accessed the climate change effects on the distributions of warm-evergreen broad-leaved trees (shorten to warm-evergreens below) in the Korean Peninsula (KP). For this, we first selected nine warm-evergreens with the northern distribution limits at mid-coastal areas of KP and climate variables, coldest month mean temperature and coldest quarter precipitation, known to be important for warm-evergreens growth and survival. Next, species distribution models (SDMs) were constructed with generalized additive model (GAM) algorithm for each warm-evergreen. SDMs projected the potential geographical distributions of warm evergreens under current and future climate conditions in associations with land uses. The nine species were categorized into three groups (mid-coastal, southwest-coastal, and southeast-inland) based on their current spatial patterns. The effects of climate change and land uses on the distributions depend on the current spatial patterns. As considering land uses, the potential current habitats of all warm-evergreens decrease over 60%, showing the highest reduction rate for the Kyungsang-inland group. SDMs forecasted the expansion of potential habitats for all warm-evergreens under climate changes projected for 2050 and 2070. However, the expansion patterns were different among three groups. The spatial patterns of projected coldest quarter precipitation in 2050 and 2070 could account for such differences.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.61-70
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• 2020

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.