• Journal of the Korean Association of Geographic Information Studies
• /
• v.9 no.4
• /
• pp.1-11
• /
• 2006

This paper aims to develop a GIS(Geographic Information System) application model as a decision-making support system in order to evaluate the potential of forests according to their functions, or to classify forest functions. The forest functions analyzed in this study are as follows: production of timber, stable supply of water resources, forest hazards prevention, recreation in forests, conservation of living conditions and natural environment. Using a model possible to evaluate the potential of each forest function and to assort forest functions by making priority-based decisions according to the functions, as well as allowing for various possible analysis environments, its application has been reviewed. Factors for assessing the forest functions could be built by using the following three categories: four maps-topographical map, vegetation map, forest site map and basic forest land use map-whose quantitative drawings had already been made; other self-established maps, such as one indicating the location of sawmills, location map of expressway interchanges, and spatial data of national population distribution map; and attribute data of population and precipitation. The GIS application developed here contributes to the evaluation of forest functions in all the subject areas by map units and national forest management districts based upon the assessment system.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.350-358
• /
• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• The Journal of Information Systems
• /
• v.29 no.2
• /
• pp.287-306
• /
• 2020

Purpose Youth unemployment is a social problem that continues to emerge in Korea. In this study, we create a model that predicts the employment of college graduates using decision tree, random forest and artificial neural network among machine learning techniques and compare the performance between each model through prediction results. Design/methodology/approach In this study, the data processing was performed, including the acquisition of the college graduates' vocational path survey data first, then the selection of independent variables and setting up dependent variables. We use R to create decision tree, random forest, and artificial neural network models and predicted whether college graduates were employed through each model. And at the end, the performance of each model was compared and evaluated. Findings The results showed that the random forest model had the highest performance, and the artificial neural network model had a narrow difference in performance than the decision tree model. In the decision-making tree model, key nodes were selected as to whether they receive economic support from their families, major affiliates, the route of obtaining information for jobs at universities, the importance of working income when choosing jobs and the location of graduation universities. Identifying the importance of variables in the random forest model, whether they receive economic support from their families as important variables, majors, the route to obtaining job information, the degree of irritating feelings for a month, and the location of the graduating university were selected.

• Journal of Forest and Environmental Science
• /
• v.32 no.3
• /
• pp.291-301
• /
• 2016

One of the most concerned topics in ecology is the relationship between biodiversity and ecosystem functioning. However, there are few field studies, carried out in forests, although many studies have been done in controlled experiments in grasslands. In this paper, we describe the relationship pattern between three facets of diversity and productivity at Ratargul Fresh Water Swamp Forest (RFWSF) in Bangladesh, which is the only remaining fresh water swamp forest of the country. Sixty sample plots were selected from RFWSF and included six functional traits including leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), tree height, bark thickness and wood density. In analyzing TD, we used Shannon diversity and richness indices, functional diversity was measured by Rao's quadratic entropy (Rao 1982) and Faith's (1992) index was used for phylogenetic diversity (PD). It was found that, TD, FD and PD were positively related with productivity (basal area) due to resource use complementarity but surprisingly the best predictor of tree productivity was FD. The results contribute to the understanding the effects of biodiversity loss and it is essential for conservation decision-making and policy-making of Ratargul Fresh Water Swamp Forest.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.1
• /
• pp.11-16
• /
• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2019.08a
• /
• pp.183-183
• /
• 2019

• Journal of the Korean Association of Geographic Information Studies
• /
• v.14 no.2
• /
• pp.128-137
• /
• 2011

The purpose of the FAPIS(Forest Aerial Photograph Interpretation System) development is to increase accuracy and efficiency of the digital forest cover type mapping for improving conventional analog-based mapping procedures by optimizing work-flow and mapping technology. The database models including digital forest cover type map, aerial photograph, and topographic map were designed for use in this system construction. The interface configured concisely to connect with functions such as search engine, display control, conversion to stereo interpretation mode, modification tools, automation of print layout and database models. It is expected that the standardization methodology based on this system can be applied and extended in making all kinds of digital thematic maps, providing decision-making and information of forest resources.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.18 no.4
• /
• pp.378-388
• /
• 2016

International societies are currently working together to achieve the Climate-Smart Agriculture (CSA) initiative which aims the triple wins: (1) sustainably increasing agricultural productivity and incomes; (2) adapting and building resilience to climate change; and (3) mitigating greenhouse gases emissions. In terms of its scope and context, CSA follows the '3Nong (三農)' vision cast about 200 years ago by Dasan Jeong Yak-Yong who emphasized the triad of governance, management and monitoring towards comfortable, profitable and noble agriculture. Yet, the CSA provides the practical aims that facilitate the development of holistic indicators for quantitative evaluation and monitoring, on which decision-making support system is based. In this study, we introduce an agent-based model, i.e. Mathematical Programming Multi-Agent Systems (MP-MAS), as a tool for supporting the decision-making toward CSA. We have established the initial version of MP-MAS adapted for domestic use and present the preliminary results from an application to the rice farming case in Haenam, Korea. MP-MAS can support both farmers and policy-makers to consider diverse management options from multiple perspectives. When the modules for system resilience and carbon footprint are added, MP-MAS will serve as a robust tool that fulfills not only CSA but also Dasan's '3Nong' vision of sustainable agricultural-societal systems.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.3
• /
• pp.345-352
• /
• 2020

Forest fires generate many types of risk as well as a wide and varied range of damage. Various studies and systems have emerged in response to wildfire disasters. International wildfire disaster safety management systems apply advanced technologies such as utilizing big data, GIS-based systems, and decision-making systems. This study analyzes South Korea's and other countries' forest fire disaster safety management systems, and suggests alternatives for wildfire disaster safety management in Korea. First, a means of integrating information, including field information, obtained by domestic agencies is proposed. Second, a method of applying big data to the disaster response system is proposed. Third, a decision-making system is applied to an existing GIS-based system. When applying the above countermeasures to Korea's existing disaster safety management system, various information and data can be visualized and thus more easily identified, leading to more effective decision-making and reduced fire damage.

• Journal of the Korean GEO-environmental Society
• /
• v.20 no.12
• /
• pp.33-39
• /
• 2019

The upsurge of population, internal migration, economic activities and developmental works has brought significant land use and land cover (LULC) change over the period of 1990 and 2010 in the Bagmati basin of Nepal. Along with alteration on various other ecosystem services like water yield, water quality, soil loss etc. carbon sequestration is also altered. This study thus primary deals with evaluation of LULC change and its impact on the soil carbon storage for the period 1990 to 2010. For the evaluation, InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Carbon model is used. Residential and several other infrastructural development activities were prevalent on the study period and as a result in 2010 major soil carbon reserve like forest area is decreased by 7.17% of its original coverage in 1990. This decrement has brought about a subsequent decrement of 1.39 million tons of carbon in the basin. Conversion from barren land, water bodies and built up areas to higher carbon reserve like forest and agriculture land has slightly increased soil carbon storage but still, net reduction is higher. Thus, the spatial output of the model in the form of maps is expected to help in decision making for future land use planning and for restoration policies.