• KIEAE Journal
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• v.9 no.3
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• pp.21-27
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• 2009

The goal of this paper is to determine the suitability evaluation indices of a riverside reservoir space planning by classifying major indicators and calculating AHP(Analytical Hierarchy Process) based weights of them. The major indicators were set up based on literature review and questionnaire survey to experts. Four indicator categories were developed: location, environment, resource availability and economical efficiency. And they were divided into 12 sub-categories for calculating AHP-based weights. First, as for the major indicator categories, the calculation shows that the weighted index of environment is the most important at 0.458, followed by location at 0.128, economical efficiency at 0.170 and resource availability at 0.154. This suggests that environment is getting more public attention and the reservoir is regarded as a facility that is connected to a river. Those weight values were considered in calculating final weights for each of 12 sub-categories. Among them water quality and ecological environment take top ranks at 0.190 and 0.186, respectively. The lower ranks include access 0.112, resource availability of site 0.082, tourism resource 0.078, users 0.076, available land 0.052, area of site 0.031, shape of site and deterioration level 0.030 and percentage of private land 0.030 - which represents general considerations in other space planning. The difference of the top rank (water quality, 0.190) and the last one (percentage of private land, 0.027) is 0.163. The above result shows that users regard environmental aspect and resource availability more important than easiness of construction.

• Economic and Environmental Geology
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• v.35 no.1
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• pp.67-74
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• 2002

The purpose of this study is to determine the weights of the factors for landslide susceptibility analysis using artificial neural network. Landslide locations were identified from interpretation of aerial photographs, field survey data, and topography. The landslide-related factors such as topographic slope, topographic curvature, soil drainage, soil effective thickness, soil texture, wood age and wood diameter were extracted from the spatial database in study area, Yongin. Using these factors, the weights of neural networks were calculated by backpropagation training algorithm and were used to determine the weight of landslide factors. Therefore, by interpreting the weights after training, the weight of each landslide factor can be ranked based on its contribution to the classification. The highest weight is topographic slope that is 5.33 and topographic curvature and soil texture are 1 and 1.17, respectively. Weight determination using backprogpagation algorithms can be used for overlay analysis of GIS so the factor that have low weight can be excluded in future analysis to save computation time.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.499-506
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.663-672
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• 2021

Background and objective: As the 'Healing Agriculture Research and Development and Promotion Act' came into effect on March 25, 2021, social interest in agro-healing has been increasing exponentially. This study was conducted to analyze the priorities of agro-healing classification system and to provide basic data to inform policy directions and related research for the development and activation of agro-healing. Methods: The survey data collected from 18 experts were analyzed using the analytic hierarchy process (AHP) method in determine the relative weights of the main and sub-criteria for the classification system. There were three main criteria identified: agro-healing input industry, agro-healing service industry, and agro-healing-related/derived industry. There were also 11 sub-criteria. Results: The top three sub-criteria with the highest complex weights include "community service," "social rehabilitation" and "treatment and rehabilitation," all of which correspond to the main criterion "agro-healing service industry". In addition, the complex weights of the sub-criteria corresponding to the main criterion "agro-healing related/derived industry" are as follows: "other agro-healing support service," which ranked 4th, and "training and education institutions for agro-healing experts," which ranked 5th when prioritizing the criteria. Conclusion: The results of this study suggest that the main criterion to be considered first in establishing a classification system is "agro-healing service industry". Therefore, it is necessary to continue research on detailed service classifications and systems with verified validity to ensure expertise in human resources, and organization related to social purpose services in agro-healing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.4
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• pp.107-116
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• 1992

The structural integrity of bridges is mainly damaged by overloaded heavy vehicles. The increasing volumes of overloaded heavy vehicles has been indicated as serious state. As results several countries have revised their bridge load codes. However, because of variety of truck types and their weights it is difficult to develop rational standard truck loads. In addition the common practice that only one design configuration of standard truck is adopted to design variety of bridges causes further difficulties. The objective of the study is to investigate the statistical characteristics of vehicle loadings based on survey data collected, in which some major factors, such as vehicle configurations, vehicle weights, traffic modes, etc., are incorporated. The vehicle load effects due to single presence of heavy truck are also tested with several short-span bridges and probabilistic characteristics of current design practices are evaluated.

• Journal of Construction Engineering and Project Management
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• v.6 no.3
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• pp.1-7
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• 2016

The purpose of this paper is to develop a model for selecting the best contractor in the Gaza Strip using the Artificial Neural Network (ANN). The contractor's selection methods and criteria were identified using a field survey. Fifty four engineers were asked to fill a questionnaire that covers factors related to the selection criteria of contractors practiced in Gaza Strip. The results shows that the dominant part of respondents (91%) confirmed that the current awarding method "the lowest bid price" is considered one of the major problems of the construction sector, "award the bid to the highest weight after combination of the technical and financial scores" represented 50% of the respondents. The criteria weights were determined based on Relative Importance Index (RII. Ninety-one tenders(13 projects) were used to train and test the ANN model after re-evaluating the contractors depend on the weights of factors to select the best contractor who achieves the highest score. Neurosolution software was used to train the models. The results of the trained models indicated that neural network reasonably succeeded in selection the best contractor with 95.96% accuracy. The performed sensitivity analysis showed that the profitability and capital of company are the most influential parameters in selection contractors. This model gives chance to the owner to be more accurate in selecting the most appropriate contractor.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.294-297
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• 2004

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and then to apply these to the selected study area of Janghung in Korea. We aimed to verify the effect of data selection on training sites. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use was constructed. Thirteen landslide-related factors were extracted from the spatial database. Using these factors, landslide susceptibility was analyzed using an artificial neural network. The weights of each factor were determined by the back-propagation training method. Five different training datasets were applied to analyze and verify the effect of training. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. The results of the landslide susceptibility maps were verified and compared using landslide location data. GIS data were used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool to analyze landslide susceptibility.

• Journal of the Korean Home Economics Association
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• v.23 no.4
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• pp.33-54
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• 1985

The purpose of this study is to establish the suitable wearing standard under environmental thermal conditions in Pusan. The data is obtained from 50 girl students from April, 1984 to March, 1985. Items of the research are as follows : Environmental conditioni, clothing weight, contents of wearing, clothing climate, wearing order etc. RESULTS : 1. The upper clothing wights are varied considerably with temperature, while the lower are not. 2. The outdoor temperature and the total clothing weights show the high negative correlation of r=-.97 wth regression equation of Y=-37.64X+1692.66. 3. The clothing weight per clo is 390g/$m^2$. 4. Mostly, subjects were 2~7kinds of the upper and 3~5kinds of the lower clothing. 5. The clothing weights on the upper part of the body are heavier than those on the lower part of it. 6. The standard deviation of the obver clothing is larger than that of the under clothing. 7. The clothing shape of comfort-sensation reporter changes with variation of temperature. 8. The clothing climate of the inner layer is 32.26$\pm$$0.5^{\circ}C$ in temperature, 43.6$\pm$7% in humidity at four seasons. 9. It is represented that total subjects and comfort-sensation reporter control the wearing contents suitably for temperature. 10. The standard of wearing in Pusan is established as Fig. 6.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.77-82
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• 2003

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the developed techniques to the study area of janghung in Korea. Landslide locations were identified in the study area from interpretation of satellite image and field survey data, and a spatial database of the topography, soil, forest and land use were consturced. The 13 landslide-related factors were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods, and the susceptibility map was made with a e15 program. For this, the weights of each factor were determinated in 5 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated using the weights and the susceptibility maps were made with a GIS to the 5 cases. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to analyze the landslide susceptibility.

• Transactions of the Korean hydrogen and new energy society
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• v.20 no.1
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• pp.79-85
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• 2009

In this paper, we investigated impact factors by brainstorming and survey research and calculated the weights of them using the Analytic Hierarchy Process(AHP) method in order to evaluate alternatives for supplying Liquefied Natural Gas(LNG). AHP is a useful method for evaluating multi-criteria decision making problems. We selected 3 criteria and 9 sub-criteria. According to the result in this study, the most important sub-criterion is the Government's Policy, and the second is the Province's Policy. The other side, the lowest important sub-criterion is the Investment Cost. This study may provide basic data to select the optimal alternative for supplying LNG.