• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.41-49
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• 2023

To utilize the hydraulic and hydrological models when simulating floods in agricultural watersheds, it is necessary to consider agricultural reservoirs, farmland, and farmland drainage system, which are characteristics of agricultural watersheds. However, most of them are developed individually by different researchers, also, each model has a different simulation scope, so it is hard to use them integrally. As a result, there is a need to link each hydraulic and hydrological model. Therefore, this study established an integrated flood simulation system for the comprehensive flood simulation of agricultural reservoir watersheds. The system can be applied easily to various watersheds because historical weather data and the SSP (Shared Socio-economic Pathways) climate change scenario database of ninety weather stations were built-in. Individual hydraulic and hydrological models were coded and coupled through Python. The system consists of multiplicative random cascade model, Clark unit hydrograph model, frequency analysis model, HEC-5 (Hydrologic Engineering Center-5), HEC-RAS (Hydrologic Engineering Center-River Analysis System), and farmland drainage simulation model. In the case of external models with limitations in conceptualization, such as HEC-5 and HEC-RAS, the python interpreter approaches the operating system and gives commands to run the models. All models except two are built based on the logical concept.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.65-92
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• 2017

Purpose: The performance of global major airports is evaluated and several research questions are examined relative to the measures characterizing airport performance. Methods: The two-stage internal structure of airport performance is considered by decomposing it into physical operations and revenue generation. In the physical operations stage, operating costs, number of runways, terminal area and number of employees are used as inputs, while passenger throughput, cargo throughput and aircraft movements are taken as outputs. Subsequently, in the revenue generation stage, the outputs from the preceding stage are taken as inputs, while revenue is used as output. Results: Based upon this two-stage modeling of airport performance, a multiplicative two-stage network data envelopment analysis model is employed to calculate the overall and stage efficiencies of 59 airports using the recent data in the 2014 Airport Benchmarking Report published by the Air Transport Research Society. Several internal and external factors are also considered such as airport size, airport geographical location, proportion of international passengers, ownership (listed or not) and management style, and statistical analysis is performed to examine their impacts on airport performance. Conclusion: It is shown that the airports exhibit statistically significant difference across regions, and also some statistically significant factors affecting airport performance are identified.

• Journal of environmental and Sanitary engineering
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• v.7 no.2
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• pp.141-152
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• 1992

Simple correlation analysis, factor analysis, and multi-verite analysis have been performed to analyze the relationship between air pollution and meteorological factors for air pollution and meteorological data measured at Kwanghwamun in Seoul during the period of one year (January 1990" December 1990). As a result of simple correlation and factor analysis, $SO_2$, TSP and CO concentrations have shown high negative correlation with temperature and among these indicating that these are related with pollutants emission trend based upon heating fuel usage. Ozone has a good correlation with solar radiation and relative humidity to have a closed relation with $0_3$ generation reaction mechanism. The result of multi-variate correlation analysis shows that the concentration of $SO_2$ and CO are adequate for correlation model with ambient temperature and wind speed and 0$_{3}$ concentrations are adequate for that with solar radiation and wind speed. $SO_2$ and CO levels are considered to be affected first of all by heating fuel usage as a emission source and wind speed as a dispersion effect. The $SO_2$ concentration in the condition that the temperature fall below zero is explained by multiplicative model with wind speed, only one variable.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1774-1786
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• 2018

Phenotypic variation among clones (individuals with identical genes, i.e. isogenic individuals) has been recognized both theoretically and experimentally. We investigate the effects of phenotypic variation on evolutionary dynamics of a population. In a population, the individuals are assumed to be haploid with two genotypes : one genotype shows phenotypic variation and the other does not. We use an individual-based Moran model in which the individuals reproduce according to their fitness values and die at random. The evolutionary dynamics of an individual-based model is formulated in terms of a master equation and is approximated as the Fokker-Planck equation (FPE) and the coupled non-linear stochastic differential equations (SDEs) with multiplicative noise. We first analyze the deterministic part of the SDEs to obtain the fixed points and determine the stability of each fixed point. We find that there is a discrete phase transition in the population distribution when the probability of reproducing the fitter individual is equal to the critical value determined by the stability of the fixed points. Next, we take demographic stochasticity into account and analyze the FPE by eliminating the fast variable to reduce the coupled two-variable FPE to the single-variable FPE. We derive a quasi-stationary distribution of the reduced FPE and predict the fixation probabilities and the mean fixation times to absorbing states. We also carry out numerical simulations in the form of the Gillespie algorithm and find that the results of simulations are consistent with the analytic predictions.

• Culinary science and hospitality research
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• v.15 no.2
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• pp.231-242
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• 2009

One of the most crucial information at the hotel banquet is revenue data. Revenue forecast enables cost reduction, increases staffing efficiency, and provides information that helps maximizing competitive advantages in unforeseen environment. This research forecasts the hotel banquet revenue by utilizing ARIMA Model which was assessed as the appropriate forecast model for international researches. The data used for this research was based on the monthly banquet revenue data of G hotel at Seoul. The analysis results showed that SARIMA(2, 1, 3)(0, 1, 1) was finally presumed. This research implied that the ARIMA model, which was assessed as the appropriate forecast model, was applied for analyzing the monthly hotel banquet revenue data. Additionally, the research provides beneficial information with which hotel banquet professionals can utilize as a reference.

• Korean Journal of Ichthyology
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• v.18 no.1
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• pp.45-54
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• 2006

Age and growth of the small yellow croaker, Larimichthys polyactis were estimated using right sagittal otoliths of 506 fish specimens from March to December, 2002 and from January to February, 2005 in the South Sea, part of the East China Sea of Korea. Examination of outer margins of the otolith showed that the opaque zone was formed once a year. Marginal increment of the otolith formed annual rings from May and June at the beginning of spawning season. In the relationship between total length and body weight, a multiplicative error structure was assumed because variability in growth increased as a function of the length, and the estimated equation was $BW=0.0044TL^{3.2502}$ ($R^2=0.97$). The relative growth as body weight at total length has significant difference between females and males (P<0.05). For describing growth of the small yellow croaker, Larimichthys polyactis a von Bertalanffy growth model was adopted. The von Bertalanffy growth curve had an additive error structure and the growth parameters estimated from non-linear regression were $L_{\infty}=33.88cm$, K=0.20/year and $t_0=-2.39year$. Growth at age of males and females shows no significant difference (P>0.05). Most examined fish were 1, 2 and 3 years old, although the oldest fish were 7 old for males and 8 for females.

• Journal of Agricultural Extension & Community Development
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• v.20 no.2
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• pp.511-533
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• 2013

The study used a seasonal ARIMA model to forecast the number of tourists to Kyeongju foreign in a uni-variable time series. Time series monthly data for the investigation were collected ranging from 1995 to 2010. A total of 192 observations were used for data analysis. The date showed that a big difference existed between on-season and off-season of the number of foreign tourists in Kyeongju. In the forecast multiplicative seasonal ARIMA(1,1,0) $(4,0,0)_{12}$ model was found the most appropriate model. Results show that the number of tourists was 694 thousands in 2011, 715 thousands in 2012, 725 thousands in 2013, 738 thousands in 2014, and 884 thousands in 2015. It was suggested that the grasping of the Kyeongju forecast model was very important in respect of how experts in tourism development, policy makers or planners would establish marketing strategies to allocate services in Kyeongju as a tourist destination and provide tourism facilities efficiently.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.271-280
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• 2009

For bivariate binomial data with both intra and inter-class correlation, Danaher and Hardie (2005) proposed a bivariate beta-binomial model. However, the model is limited to the situation where the intra-class correlation is strictly positive. Thus it might be seriously inadequate for data with a negative intra-class correlation. Several authors have considered generalized binomial distributions covering a wider range of intra-class correlation which could relax the possible model restrictions imposed. Among others there are the additive/multiplicative and the beta/extended beta binomial model. In this study, bivariate models of the Sarmanov (1966) type are formed by combining each of those univariate models to take care of the inter-class correlation, and are evaluated in terms of the goodness-of-fit. As a result, B-mB and B-ebB are fitted, successfully, to real data and that B-mB, which has a wider permissible range than B-ebB for the intra-class correlation is relatively preferred.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.8
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• pp.1119-1126
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• 2013

The objective of this study was to compare the goodness of fit of four lactation curve models: Wood's Gamma model (WD), Wilmink (WL), and Pollott's multiplicative two (POL2) and three parameters (POL3) and to determine the environmental factors affecting the complete lactation curve of F1 dairy sheep under organic management. A total of 5,382 weekly milk yields records from 150 ewes, under organic management were used. Residual mean square (RMS), determination coefficients ($R^2$), and correlation (r) analysis were used as an indicator of goodness of fit for each model. WL model best fitted the lactation curves as indicated by the lower RMS values (0.019), followed by WD (0.023), POL2 (0.025) and POL3 (0.029). The four models provided total milk yield (TMY) estimations that were highly correlated (0.93 to 0.97) with observed TMY (89.9 kg). The four models under estimated peak yield (PY), whereas POL2 and POL3 gave nearer peak time lactation estimations. Ewes lambing in autumn had higher TMY and showed a typical curve shape. Higher TMY were recorded in second and third lambing. Season of lambing, number of lambing and type of lambing had a great influenced over TMY shaping the complete lactation curve of F1 dairy sheep. In general terms WL model showed the best fit to the F1 dairy sheep lactation curve under organic management.