• ETRI Journal
• /
• v.37 no.2
• /
• pp.306-316
• /
• 2015

In today's highly competitive business environment, most companies try to manage their logistics function strategically to satisfy their orders as cost-effectively as possible and maximize their present and future profits. In this environment, logistics process visibility is essential to companies wishing to competently track components, parts, or products in transit from the original suppliers to the final destinations. Thus, it is important to provide instantly and easily recognizable information about such visibility to all stakeholders, especially customers. To ensure a high-level geographical visibility of the logistics processes, in this paper, we propose a way of implementing a GIS-based logistics process monitoring environment and of integrating a performer's work processing environment on the monitoring system. Additionally, to provide more abundant monitoring information, we describe a procedure for creating and processing various monitoring information, which are represented as ECA rules in this work processing environment. Therefore, it is possible to provide intuitive and visible monitoring information regarding the logistics process and resource elements.

• Journal of The Geomorphological Association of Korea
• /
• v.27 no.3
• /
• pp.87-103
• /
• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.