• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.19-27
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• 2020

This research investigates the determinants of investment capital size in Vietnam's small and medium-sized firms. The study employs a sample of 458 small and medium-sized enterprises in the country. The study is based on data collects in the areas of Hanoi, Bac Can, Buon Ma Thuot and Pleiku Provinces at time series data of October 2019. This study also identifies the factors that affect the size of investment capital in medium and small-sized enterprises in Vietnam. Data are processed via STATA 14.0 and SPSS 20.0 software. The research results indicate that (1) business lines, (2) import and export business, (3) type of business registration, (4) business location, (5) operating time, and (6) the percentage of the organization's capital contribution are factors that impact on the size of the investment capital of the business. Business line and business location have negative impacts on investment capital size. The operating time, the percentage of the organization's capital contribution, import and export business, and the type of business registration have positive impacts on investment capital size. In addition, the findings of this study also suggest that the operation time has the highest impact on investment capital size of the small and medium-sized firms in Vietnam.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.68-79
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• 2018

The Ki-Jang research reactor (KJRR), a new research reactor in Korea, is being planned to fulfill multiple purposes. In this study, as an assessment of the environmental radiological impact, we characterized the atmospheric dispersion and deposition of radioactive materials released by an unexpected incident at KJRR using the weather research and forecasting-mesoscale model interface program-California Puff (WRF-MMIF-CALPUFF) model system. Based on the reproduced three-dimensional gridded meteorological data obtained during a 1-year period using WRF, the overall meteorological data predicted by WRF were in agreement with the observed data, while the predicted wind speed data were slightly overestimated at all stations. Based on the CALPUFF simulation of atmospheric dispersion (${\chi}/Q$) and deposition (D/Q) factors, relatively heavier contamination in the vicinity of KJRR was observed, and the prevailing land breeze wind in the study area resulted in relatively higher concentration and deposition in the off-shore area sectors. We also compared the dispersion characteristics between the PAVAN (atmospheric dispersion of radioactive release from nuclear power plants) and CALPUFF models. Finally, the meteorological conditions and possibility of high doses of radiation for relatively higher hourly ${\chi}/Q$ cases were examined at specific discrete receptors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of Environmental Impact Assessment
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• v.18 no.2
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• pp.111-122
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• 2009

The impact of Korea's joining the Convention on the Conservation of Migratory Species of Wild Animals(CMS) was analyzed to examine its merits and faults as well as to discuss Korea's opportunities. Results of the analysis based on the agenda and decisions of the Conferences of the Parties, the parties performance, and other committees meetings over the last decades indicated that the affiliation of Korea into the CMS could provide various advantages and opportunities for Korea. First, Korea could upgrade its conservation activities regarding migratory species to the global aspects. Second, Korea could take initiatives for conservation of the migratory species in the Far East Asia. Third, Korea would have a better system in forecasting and problem-solving against the epidermic Avian Influenza through systematic cooperation with the CMS parties and other related international regimes. Finally, Korea will be in a better position to generate statistical data and to develop techniques to reduce the by-catches of the sharks and whales. Korea has already provided a fair and protective institutions for most of the migratory endangered species listed under Appendix I and II of the CMS. This implies that Korea may not require additional major changes to the basic acts and/or legislation. Joining the CMS may negatively impact on the fisheries and related businesses related to whales and sharks around the Ulsan and Pohang provinces. However, the obligation to protect whales and sharks demanded by the CMS is regarded as an acceptable article in Korea according to the analysis of the existing policies and scientific aspects. Nevertheless, if the joining the CMS should generate irreversible hardship for local people's livelihood and cultural aspects, Korea may ask for reservations on particular activities. Overall, we suggest that by joining the CMS, Korea could see various advantages and promotion in national policy.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.45-58
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• 1991

The importance of marketing mix variables, replacement demand, and competition in a new product growth model has been cited by many researchers. In this paper, these factors are integrated with an aim to model company sales of competing durables. Based on the most popular new product growth model, numerous extensions and incorporations of contributions from related research fields are tried. Model parameters are estimated by the Kalman filter. And, the proposed model is applied to the sales of four consumer durable goods. Empirical applications show the benefits, as well as the limitations of the proposed model.

• Journal of Contemporary Eastern Asia
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• v.15 no.2
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• pp.177-190
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• 2016

One of the main objectives of this paper is to provide insight to understand the effect of natural disasters on local government finance. That is, to analyze local governments' sales tax revenues after Hurricane Ike. Three Texas cities are examined: League City, Pearland, and Sugarland. Based on data collected from the Texas Comptroller's Office and the US Census, we found local governments experience a short-term increase in sales tax revenues and a long-term decline after the hurricane strike the region. On average, a major hurricane has a two-year impact on local government economy. The findings are essential for practitioners because in order to have a prosperous recovery after natural disasters, public managers have to prepare financially for short term changes in their sales tax revenues.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1015-1022
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• 2009

Currently, Korean real estate market has experienced cooling down of the business because of the global economic crisis which resulted from the subprime mortgage lending practice. In response, the Korean government has enforced various policies at the base of deregulating real estate speculation, such as increasing Loan to value ratio (LTV) in order to stimulate housing demand and supply. However, these policies seemed to result in deep confusion in the Korean housing market. Furthermore, analysis for housing market forecasting, especially international financial crisis on Korean real estate market, has been partial and fragmentary, therefore comprehensive solution and systematical approach is required to analyze the real estate and real estate financial market including causal nexus between market determining factors. In an integrated point of view, applying the system dynamics modeling, the paper aims at proposing Korean Real Estate and Mortgage market dynamics models based on fundamental principles of housing market determined by supply and demand. We also find the impact of deregulation policies focusing on mortgage loan which is the main factors of policies.

• Ocean and Polar Research
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• v.33 no.4
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• pp.497-506
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• 2011

The rapid industrial and technological development has made the human activities for the utilization of marine resources more complex. Marine spatial management is a space-based approach. It is a comprehensive and integrated management approach. The ultimate goal of marine spatial management is the "sustainable use" of marine resources. The partial approach is applied in the existing marine spatial management, mainly coastal zones which involves integrated approach. Also this showed various limitations including restricted mostly to coastal zones, and limitation to implementation tools. However, for marine spatial management to have a reasonable approach that attaches importance to the relationship between humans and the holistic ecosystem, it is important to internalize a central principle in marine spatial management that focuses on the sustainable use of marine resources. In the present study, four central principles are proposed that will eventually be applied through marine spatial management planning tools. These principles are 1) the establishment of a cooperative decision making and planning system that is based on stakeholder participation; 2) scientific assessment of the current status and impact on the basis of ecology, sociology, and economics; 3) reasonable and optimal spatial assignment based on the forecasting of future-use characteristics and environmental changes; and 4) ascribing importance to the implementation of the results of rational planning processes.

• Journal of Environmental Impact Assessment
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• v.19 no.4
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• pp.381-396
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• 2010

It is essential to consider strategies, spatial planning, and reflection of sustainability for the creation of sound urban spaces. To this end, there is a need for plans that can secure better sustainability through strategic environmental assessment (SEA) of plans. This study examined the literature and available precedent to develop a SEA model for administrative plans for urban development including metropolitan plans, urban master plans and urban management plans. In the course of development of the model, environmental issues associated with the urban plans were analyzed by classifying them into ten categories, including "spatial planning," "conservation planning," "greenbelt systems," "habitats." and etc. according to their rank. Furthermore, those issues were reflected on the development of environmental evaluation indices for the plans. Overall and detailed environmental indices that can be applied to the administrative plans for urban development including metropolitan plans, urban master plans and urban management plans were devised for five stages: (1) Establishment of development goals and strategy, (2) Analysis of current status and characteristics, (3) Conceptualization of spatial structure, (4) Planning for each department, and (5) Execution and management. Sub plans are more detailed and concrete. Criteria based on the evaluation indices, when performing evaluations on plans based on each environmental assessment index in reference to experts and the literature, were used to forecast their effects, i.e. whether they had a positive, negative, or no effect or relationship, or whether their effects was uncertain. Based on the forecasts, this study then presents means to establish more improvable plans. Furthermore, by synthesis of the effects according to each index and integration of the process, plans were analyzed overall. This study reflects the characteristics of the present time period based on issues in the SEA process and techniques in upper level administrative plans being newly established, and presents them according to the stage of each plan. Furthermore, by forecasting the effect of plans by stage, this study presents proposals for improvement, and in this aspect, can be meaningful in promoting plan improvements through SEA.