1. The Utilization of GIS for the Measure against Slope Failure Disaster in Urban Area Akiyuki KAWASAKI, and Satoru SADOHARA Yokohama National University, Yokohama, Japan

2. Background ⇒ It ’ s difficult for a municipality to afford it. ・ Many people and much budget are required to accomplish this kind of project (so far). ⇒ Municipality needs a measure against slope failure disaster ・ Many slope failure disasters happen in Yokohama

3. (the ultimate goal) create by existing data and documents. ・ To create a “Real-time evaluation system against slope failure disasters” by existing data and documents in municipal government. with a small budget and people. ⇒ It will reduce the damage of slope failure disasters with a small budget and people. Objective

4. A ． Specifying the “steep slope” and “the predicted damage area” automatically using GIS raster & vector analysis. A ． Specifying the “steep slope” and “the predicted damage area” automatically by DEM and LandUse data using GIS raster & vector analysis. B. the dangerous area for slope failure by rainfall data at the time using GIS spatial analysis. B. Predicting the dangerous area for slope failure by rainfall data at the time using GIS spatial analysis. For that objective, three methodologies were carrying forward in this study. (Presented last year) C. Evaluate the possibility of slope failure disaster, using Inspection Record and Multivariate Analysis. ( this year) (Presented this year)

5. Steep Slope Predicted-damaged area Software: ArcInfo Workstation & ArcView LandUse DEM Steep slope Predicted damaged area A ． Methodology of specifying the “steep slope” and “the predicted damage area” automatically. (Review) This is helpful to predict which building and house would be damaged by collapse. Only two data is required.

6. A ． Methodology of specifying the “steep slope” and “the predicted damage area” automatically. (Review) These are the specified dangerous steep slopes ⇒ We verified that it covers more than 90% of (existing) “ Municipality’s dangerous areas”

7. A ． Methodology of specifying the “steep slope” and “the predicted damage area” automatically. (Review) Predicted damage area is calculated by the height of the slope. It means that higher steep slope has longer damaged area.

8. A ． Methodology of specifying the “steep slope” and “the predicted damage area” automatically. (Review) Predicted damage area is calculated by the height of the slope. It means that higher steep slope has longer damaged area.

9. B. Methodology of p the dangerous area for slope failure by rainfall data at the time. B. Methodology of predicting the dangerous area for slope failure by rainfall data at the time. (Review) Dangerous Area at the time by Rainfall data （ August/22th/2001 ） 2.00 am6.00 am10.00 am2.00 pm 1 hour rainfall (mm) Y = ‐ 0.174X + 28.4 (“Raster Calculation” )

10. A ． Dangerous steep slope and predicted damage area are specified. B. Relationship between collapse and rainfall are analyzed. C. Relationship between collapse and the slope’s factors. ⇒ Provoking cause 《 Triggered factor 》 for collapse ⇒ Primary cause 《 Basic factor 》 for collapse For the objective, three methodologies were carrying forward in this study. ⇒ Automatic specification.

11. Evaluate the primary cause (basic factor), using Inspection Report and Multivariate Analysis; Quantification Theory Type Ⅱ. Easy to collapse Difficult to collapse C. Relationship between collapse and the slope’s factors.

12. (Provided by City of Yokohama, Bureau of Building) Inspection Report for a Steep Slope - Height - Slope - Overhang - Surface thickness -Leaking water - Vegetation - Looseness, relaxation - Surface water - Drainage on the top quantified 10 items are quantified by report ’ s category. Collapse record

13. Multivariate Analysis; Quantification Theory Type Ⅱ quality “ explanatory variable (item) ” quality “ objective variable ” quantity This model clarify quality “ explanatory variable (item) ” affecting quality “ objective variable ”,as a quantity weighted coefficient. “ explanatory variable (item) ” “ explanatory variable (item) ” : 10 items on inspection report “ objective variable ” “ objective variable ” : Collapse record (collapsed or not) Example of analysis result

14. Range Factor’s effectiveness for collapse Slope Failure Pattern in Yokohama (Categorized by “Yokohama city slope failure committee”)

15. Results of Quantification Theory Type Ⅱ Sample score Relative frequency Frequency distribution of distinction result Actual collapsed No collapse Discriminate Value – 0.06 ( Accuracy 85.1%) Pattern 1 Sample score : Total of weighted coefficient of a slope 0.086+(-0.107)+(-0.078)+1.296+ …… =0.677

16. Slope Failure Pattern in Yokohama (Categorized by “Yokohama city slope failure committee”) Discriminate Value – 0.06 (Accuracy 85.1%) Discriminate Value – 0.01 (Accuracy 84.2%) Discriminate Value 0.01 (Accuracy 82.1%) Frequency distribution of distinction result

17. - Factors and the category ’ s effectiveness for collapse were clarified quantitatively. - Accuracy of distinction for collapse by sample score was over 80% ⇒ Municipality ’ s Inspection report woks adequately to evaluate the possibility of collapse. C. Relationship between collapse and the slope’s factors.

18. Conclusion 1. ⇒ By combining these three, a “ Real-time evaluation system against slope failure disasters ” would be completed A ． Dangerous steep slope and predicted damage area are specified. B. Relationship between collapse and rainfall are analyzed. C. Relationship between collapse and the slope’s factors. ⇒ Provoking cause 《 Triggered factor 》 for collapse ⇒ Primary cause 《 Basic factor 》 for collapse ⇒ Automatic specification.

19. １． Specifying the Dangerous Area by Rain Fall Data ２． Possibility of Slope Failure ３． Specifying the Predicted Damage Area Future works

20. Conclusion 2. All the methodology in this study was from the existing data and document in municipal government. ⇒ Original data and document are existed separately in each bureau and section. In this study, all the data was digitized and connected. “e-government” and “e-municipal government” are declared in Japan. ⇒ This is an example to show the efficiency of digitizing and utilizing the existing data and document in municipal government.

21. Akiyuki KAWASAKI, and Satoru SADOHARA Yokohama National University, Yokohama, Japan E-mail: akiyuki@arc.ynu.ac.jp sato610@arc.ynu.ac.jp Acknowledgement - Risk management office & Building Bureau, City of Yokohama - Prof. Midorikawa & Prof. Okimura, Yokohama city slope failure committee