Geoscience Reference
In-Depth Information
6 Conclusion
In this chapter, a cluster spatial pattern of shallow landslide events were tested and
confirmed in a landslide prone area in Malaysia. Generally, different spatial pat-
terns are tested to determine whether landslides pattern rejects the independency of
spatial pattern or not (i.e. random or cluster distribution). In the present study, a
2nd order statistical test of nearest neighbor index was applied and found that
landslides registered over 25 years in Kuala Lumpur and vicinity areas have a
cluster nature pattern. In order to validate our findings, 15 causative factors were
prepared by using EBF model with the 132 cluster landslides only, and then the
layers were combined and produced LSM2. The current map was compared with
the previous work, which prepared LMS1 using EBF model with 153 random
landslides selection. LMS2 showed higher prediction rate of area under the curve
(AUC) than the previous technique of LMS1, with 0.8 and 0.75 rates respectively.
The results showed the tendency of the landslide to cluster in high density
locations with 88 % of the data rather than random pattern of other 12 % locations
in surrounding areas. The current research recommended to pre-processing the
inventory to find a common space or time distribution relationship. The future
research will focus on categorization and classification of the cluster groups by
taking into consideration of; failure types, volume, shape, and time of occurrence.
The additional data will enhance the model prediction accuracy result from general
assessment to local details and more specific slope failure zoning. Also, it will
reduce the uncertainty in particularly in the zones of high to moderate hazardous
landslide prone areas.
Acknowledgments The authors gratefully acknowledge the financial support from the UPM-
RUGS project grant, vote number: 9344100 with additional support from FIG grant.
References
Akgun A, Kıncal C, Pradhan B (2011) Application of remote sensing data and GIS for landslide
risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess
184(9):5453-5470
Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB
program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy
algorithm. Comput Geosci 38(1):23-34
Althuwaynee OF, Pradhan B, Lee S (2012a) Application of an evidential belief function model in
landslide susceptibility mapping. Comput Geosci 44:120-135
Althuwaynee OF, Pradhan B, Mahmud AR, Yusoff, ZM, (2012b) Prediction of slope failures
using bivariate statistical based index of entropy model. In: IEEE colloquium on humanities,
science and engineering (CHUSER), 2012, IEEE, pp 362-367
Bai S, Lü G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for
landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62:139-149
Carranza EJM (2009) Controls on mineral deposit occurrence inferred from analysis of their
spatial pattern and spatial association with geological features. Ore Geol Rev 35:383-400
