Pitfall in Subrogating Slope Maps for
Landslide Hazard Maps
Hazard Potential Index :
The hazard potential indices H1, H2, H3, H4, H5 and H6 for different slope classes were computed as defined below. Same data when analyzed using all the six criteria would yield hazard potential as shown in Fig. 2.
Figure 2 Index of hazard potential based on analysis of observed data and theoretically estimated (sin 0) curve
| H1 =
| Value of Criterion 1 (C1) for the slope class
-------------------------------------------
Value of Criterion 1 (C1) for the slope class1
|
| H2 =
|
Value of Criterion 2 (C2) for the slope class
-------------------------------------------
Value of Criterion 2 (C2) for the slope class1
|
| H3 =
|
Value of Criterion 3 (C3) for the slope class
-------------------------------------------
Value of Criterion 3 (C3) for the slope class1
|
| H4 =
|
Value of Criterion 4 (C4) for the slope class
-------------------------------------------
Value of Criterion 4 (C4) for the slope class1
|
| H5 =
|
Value of Criterion 5 (C5) for the slope class
-------------------------------------------
Value of Criterion 5 (C5) for the slope class1
|
| H6 =
|
Value of Criterion 6 (C6) for the slope class
-------------------------------------------
Value of Criterion 6 (C6) for the slope class1
|
The results clearly show that the overly of landslide inventory map on a slope map yield higher hazard potential for slope range between 30o -90o when compared to the theoretical values, and the reverse is the case for slope value below 30o.
Discussion
The question therefore arose if the observed trend and its variance with the theoretically estimated values were because of the limited population of data. The studies were continued to cover larger areas. The results are summarized in Fig. 3.
Figure 3 Index of hazard potential based on analysis of observed data and theoretically estimated (sin 0) curve
It is abundantly clear from the figure that increasing population of data provides better correspondence between the observed hazard potential index and the theoretical values, when slope angle is greater than 40o. Below slope angle or 40o the variance was found to be higher
It is quite possible that when the mapping continues on the cover the entire 12,000 sq. km of the hilly region emerge. In the mean time, it was considered prudent to go by the theoretically estimated values because it became absolutely clear that the observed landslides did reflect the combined effect of all the various causative factors, and not of just the slope angle along. Therefore, it would not be appropriate to rely too heavily on the observed behavior, without providing satisfactory explanation for the trend.
Impact on the Overall Hazard Potential:
The next step was to rework the
landslide hazard maps using relative weightings based on the theoretically assigned weightings, (Table 1). A much better co-relation was found between the landslide inventory map and the map of inferred instability map obtained by integration of factor maps.
Conclusion:
The paper leads to the following concluding remarks.
-
landslide hazard mapping based on integration of the factor maps demand that relative weightless are five to major causative factors as well s to sub-factors. These are usually done based on super position of factors maps on landslide inventory map. In the case of landslide hazard maps for Sri Lanka, it has been observed that relative weightings assigned to different slope classes based on field observations provide poorer convergence between the observed and inferred slope failure potential. On the other hand, if the relative weightings are based on theoretically estimated values, a better convergence is obtain.
-
With increasing population of data, it appears that the co-relation between the observed and theoretical weightings improves. Whereas the improvement is marked above slope angle of 40o, it is unremarkable below this value of slope. The explanation to this behavior may perhaps lie in the analysis of greater population of data, when such data become available.
- It is recommended that the relative weightings of the different slope classes be decided on theoretical basis till such time results of large population of field data from landslide sites become available. The Sri Lankan mapping programme covering 1200 sq km of area supports this view.
Acknowledgement
The authors would like to thank Mr. M.P.K. Perera, Head of the Computer Division at the NBRO, for providing necessary software facilities, and MR. D.M.L. Bandara of the same Division for his support in preparation of this paper.
