DOI QR코드

DOI QR Code

폭풍해일 침수예상도 검증을 위한 형상유사도 분석 : 형상기준

Shape Similarity Analysis for Verification of Hazard Map for Storm Surge : Shape Criterion

  • 투고 : 2019.09.05
  • 심사 : 2019.09.23
  • 발행 : 2019.09.30

초록

실시간 재난위험도 예측 모델인 SIND 모델의 정확도 확인 및 검증을 위해 다양한 형상유사도 개념을 적용하였다. 기하학적 방법론 중에서 가장 널리 이용되는 CRITIC 기법은 침수예상도와 같은 복잡한 지형 형상에 적용하기에는 분명한 한계점을 보여서 본 연구에서는 복잡한 전파특성의 형상을 평가할 수 있는 RCCI와 TF 등과 같은 형상인자를 추가하여 수정된 CRITIC 기법을 제시하였다. 본 연구에서 제안된 형상유사도 평가 방법을 폭풍해일의 침수예상도에 적용하여 검토한 결과, 면 객체 쌍들을 수동으로 정 매칭쌍과 오 매칭쌍으로 구분하였으며, 각 형상 인자들, 위치기준, 면적기준, 형상 기준의 가중치들을 변화시켜가며 각 매칭쌍의 형상유사도를 산정하였다. 본 연구에서 제안된 방법론과 산정된 가중치를 참고자료인 침수예상도의 지도 객체와 목표자료인 SIND 모델결과의 객체에 적용한 결과, 정 매칭쌍은 약 90%가 형상유사도 0.5 이상의 값을 가졌고, 오 매칭쌍은 약 70%가 0.5 미만으로 나타났다. 향후 다수의 객체가 하나의 객체와 대응되는 점을 보완 조정한다면 정 매칭쌍의 형상유사도는 전체적으로 증가하고 오 매칭쌍의 형상유사도는 감소할 것이라 판단된다.

The concept of shape similarity has been applied to verify the accuracy of the SIND model, the real-time prediction model for disaster risk. However, the CRITIC method, one of the most widely used in geometric methodology, is definitely limited to apply to complex shape such as hazard map for coastal disaster. Therefore, we suggested the modified CRITIC method of which we added the shape factors such as RCCI and TF to consider complicated shapes. The matching pairs were manually divided into exact-matching pairs and mis-matching pairs to evaluate the applicability of the new method for shape similarity into hazard maps for storm surges. And the shape similarity of each matching pair was calculated by changing the weights of each shape factor and criteria. Newly proposed methodology and the calculated weights were applied to the objects of the existent hazard map and the results from SIND model. About 90% of exact-matching pairs had the shape similarity of 0.5 or higher, and about 70% of mis-matching pairs were it below 0.5. As future works, if we would calibrate narrowly and adjust carefully multi-objects corresponding to one object, it would be expected that the shape similarity of the exact-matching pairs will increase overall while it of the mis-matching pairs will decrease.

키워드

참고문헌

  1. Ali, A. B. H. (2001). Positional and Shape Quality of Areal Entities In Geographic Databases: Quality Information Aggregation Versus Measures Classification. Proceeding of ECSQARU '2001 Workshop on Spatio-Temporal Reasoning and Geographic Information Systems. Toulouse. 1-16.
  2. Arkin, E. M., Chew, L. P., Huttenlocher, D. P., Kedem, K., and Mitchell, J. S. B. (1991). An Efficiently Computable Metric for Comparing Polygonal Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 13(3): 209-216 https://doi.org/10.1109/34.75509
  3. Burghardt, D. and Steiniger, S. (2005). Usage of Pricipal Component Analysis in the Process of Automated Generalisation. Proceedings of ICA Conference. A Coruiia. SPAIN.
  4. Fu, Z. and Wu, J. (2008). Entity Matching in Vector Spatial Data. Proceeding of the XXIth ISPRS Congress. 3-11 Jul 2008. Beijing. China. 1467-1472.
  5. Huang, L., Wang, S., Ye, Y., Wang, B., and Wu, L. (2010). Feature Matching in Cadastral Map Integration with a Case Study of Beijing. Proceedings of 2010 18th International Conference on Geoinformatics. Peking University. Beijing. China. 1-4.
  6. Huh, Y. and Yoo, K. Y. (2012). Shape Similarity Measure for M:N Areal Object Pairs using the Zernike Moment Descriptor. Journal of the Korean Society of Surveying, Geodesy, Photgrammetry, and Cartography. 30(2): 153-162. https://doi.org/10.7848/ksgpc.2012.30.2.153
  7. Kim, D. H., Yoo, H. J., Jeong, S. I., and Lee, S. O. (2018). Development for Prediction Model of Disaster Risk Through Try and Error Method : Storm Surge. Journal of Korean Society of Disaster & Security. 11(2): 37-43 https://doi.org/10.21729/KSDS.2018.11.2.37
  8. Kim, J. H., Cho, C. M., and Chae, M. K. (2006). A Study on the Application of Land Form Indices to the Standardization of Development Available Lands, Using GIS. Journal of the Korean Society of Surveying, Geodesy, Photgrammetry, and Cartography. 24(1): 99-110.
  9. Kim, J. Y., Huh, Y., Kim, D. S., and Yoo, K. Y. (2011). A New Method for Automatic Areal Feature Matching Based on shape similarity using CRITIC method. Journal of the Korean Society of Surveying, Geodesy, Photgrammetry, and Cartography. 29(2): 113-121. https://doi.org/10.7848/ksgpc.2011.29.2.113
  10. Korea Hydrographic and Oceanographic Agency. (2014). Establishment of the Coastal Inundation Maps in the Islands Region. Ocean Research Division.
  11. Samal, A., Seth, S., and Cueto, K. (2004). A Feature-based Approach to Conflation of Geospatial Sources. International Journal of Geographical Information Science. 18(5): 459-489. https://doi.org/10.1080/13658810410001658076
  12. Son, H. M., Huh, Y., and Yoo, K. Y. (2010). Geometric Shape Similarity Measure between Polygon Pairs Using the Normalized Central Moments. Conference of Korean Society for Geospatial Information. 161-162.
  13. Tong, X., Shi, W., and Deng, S. (2009). A Probability-based Multi-measure Feature Matching Method in Map Conflation. International Journal of Remote Sensing. 30(20): 5453-5472. https://doi.org/10.1080/01431160903130986
  14. Wenjing, T., Yanling, H., Yuxin, Z., and Ning, L. (2008). Research on Areal Feature Matching Algorithm Based on Spatial Similarity. Proceedings of Control and Decision Conference (CCDC 2008). China. 3326-3330.