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논문검색

Flood Inundation Prediction Model on Spatial Characteristics with Utilization of OLAP-based Multidimensional Cube Information

초록

영어

The escalation of IBS (Intelligent Building System) by convergence technologies is currently in progress with the rapid growth of data communication technology and advanced modern construction system, and on this basis, new high-density urbanization is formed. The severe problem of the formation of high-density cities is leading to the phenomenon of spatial collapse by geographical deformation, global warming and destruction of the ecosystem by climate change. Notwithstanding the fact that high accuracies are shown in the past data in terms of information on precipitation, serious vulnerabilities are shown with respect to flood preparation on the subject of natural disasters by the anomaly climate, which tends to produce an exceedingly low level of accuracy, and consequently, adverse effects by which the loss of nations’ assets and human damage are caused came about. This thesis, to bring about improvement to the drawbacks addressed, suggests a design modeling of water level prediction in relation to regional flood by constructing an OLAP-based multidimensional cube with the use of historic regional precipitation and water level data. The historic precipitation and water level data by region design the prediction model of flood inundation by extracting the pattern of the water level information after topographical relations and intelligent buildings have been increased through the extraction of multidimensional table modeling and construct of the data warehouse for the analysis of the source data via pre-processing of documents.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Infrastructure Vulnerability in Relation to Climate Change
  2.2. Prediction System for Climate Change
 3. Proposed Method
  3.1. Preprocessing of Precipitation Observation Data
  3.2. Data Analysis Process
  3.3. Structuralization of the Data Warehouse for Data Prediction
  3.4. Design of Multidimensional Prediction Model
 4. Performance Evaluation
 5. Conclusion
 Acknowledgement
 References

저자정보

  • Ji-Hoon Seo Incheon University, 119 Academy-ro, Yeonsu-gu, Incheon, Republic of Korea
  • Yoon-Ju Lee Incheon University, 119 Academy-ro, Yeonsu-gu, Incheon, Republic of Korea
  • Hye-Jin Jo Incheon University, 119 Academy-ro, Yeonsu-gu, Incheon, Republic of Korea
  • Jin-Tak Choi Incheon University, 119 Academy-ro, Yeonsu-gu, Incheon, Republic of Korea

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