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1. Extraction of potential debris source areas by logistic regressiontechnique: a case study from Barla, Besparmak and Kapimountains (NW Taurids, Turkey) 7099042001林可薇 7099042021廖珮妤 Free Powerpoint Templates

2. Outline

3. Introduction • Determination of debris accumulation zones and debris source • areas, which is one of the most crucial stages in debris flow • investigations, can be too difficult because of morphological • restrictions. • The main goal of the present study is to extract debris source • areas by logistic regression analyses based on the data from • the slopes of the Barla-Besparmak and Kapi Mountains in the • SW part of the Taurids Mountain belt of Turkey, where formation • of debris material are clearly evident and common.

4. Introduction • The study is composed of three main stages：

5. Characteristics of the study area • This region is also called as Lakes Region or the Isparta Triangle.Isparta Triangle is bounded by Antalya Gulf, and three lakes namely Burdur, Hoyran and Beysehir Lakes. • The Barla Mountain, which is constituted of a number of mountains and high hills, extends in E-W direction. Length, width and height of these mountain belt are around 30 km, 10–12 km and 2,400–2,800 m, respectively. • The Kapi Mountain limestone is represented by rudist clastic turbidicit limestone then red-beige micrite of pelagic foraminifer from base to top.

6. Characteristics of the study area • The material produced on the source area surface is released as a result of • gravitation, freezing and thawing, and pressure of water supplied by precipitation. • For this reason, assessment of precipitation is important because it contributes to • the release of material from the source area. 1995.7.13 1996.7.18 1995.7.13 1996.7.18 Senirkent Uluborlu meteorology

7. Air-photo interpretations • The study area exhibits mainly high altitudes and steep slopes. This • character does not allow easy access to each spot in the area for field • observation. • For this reason, an extensive air-photo interpretation was carried out to • extract the possible debris source areas using vertical black and white • aerial photographs of medium scale (1:35,000), dated in 1956 and 1991. • In order to make an objective assessment for debris production potential • of the lithological units, the following debris source intensity index. The debrissource intensity of any lithology (DSIi) is ratio ofthe number of pixels including debris source area ofany lithology (lithology-i) (NPDSi) to the total numberof pixels ofthe lithology-i in the whole study area(ALi).

8. Air-photo interpretations

9. Air-photo interpretations • This allows extracting possible debris source areas with the • aid of the air-photo interpretations.

10. Field investigations • An extensive field investigation was performed to check the findings obtained from • air-photo interpretations and to understand the mechanisms of debris generation.

11. Production of index maps • To assess debris source areas and to produce index maps,some of • topographical parameters. slope aspect elevation ArcView 3.2 GIS grid cells(25× 25m2) DEM(10 m) stream power index (SPI) plan curvature sediment transport capacity index (LS) profile curvature

12. Production of index maps • SPI is a measure of erosive power of water flow based on assumption that • discharge (q) is proportional to specific catchment area (As) (Moore et al. 1991). As is the specific catchment area (m2m–1), β is the slope gradient in degree. • LS is the sediment transport capacity index (Mooreand Burch 1986) the values of m and n are 0.4 and 1.3.

13. Production of index maps elevation slope plan curvature aspect

14. Production of index maps profile curvature SPI LS

15. Production of index maps

16. Production of index maps

17. Application of logistic regression technique • The fundamental principle of logistic regression is based on the analysis of a • problem in which a result measured with dichotomous variables (such as 0 • and 1 or true andfalse) is determined from one or more independent factors.

18. Application of logistic regression technique

19. Application of logistic regression technique 1. 2. 3. 4. 5.

20. Application of logistic regression technique ROC、AUC、RMSE

21. Application of logistic regression technique It could readily realized that the training data sets are evidently representative for the study area because the values given in Table 4 are very close to each other. 7.

22. Application of logistic regression technique By applyingEq. 4 with β coefficients in Table 5 to the whole data set (460,478 cases), the map of potential debris source areasis obtained (Fig. 8). Fig. 8 The produced map yielded over-predicted results.

23. Application of logistic regression technique A structural adjustment for the map of potential debris source area is needed. 9. Firstassumption Secondassumption Theoretical probability distribution of slope aspect values of debris source areas is equal to the theoretical probability distribution of being a questa in the field. It can be considered that all debris source areasmapped during field studies coincided with the geomorphologicunits of questas. is the probability of being a questa Pd’ = Pd-(1-Pq) (1-Pq) is the probability of not being a questa

24. Application of logistic regression technique • How to calculate Pq ?! Step 1 • The value of –1 in the slope aspect values does not mean orientation information(It means the flat areas) Adjustment • The value ‘‘0’’ and the value‘‘360’’ in the slope aspect distribution are equal with respect to orientation information. Step 2 Using Eq. 5, the probability values ofbeing a questa were calculated (the value ‘‘Pq’’). The adjusted potential debris source area map is obtained(Fig. 10) by using the adjusted probability values.

25. Application of logistic regression technique Before Fig. 8 V.S Fig. 10 After

26. Results and conclusions

27. 敬 請 指 教