Geoscience Reference
In-Depth Information
An Alternative Technique for Landslide
Inventory Modeling Based on Spatial
Pattern Characterization
Omar F. Althuwaynee and Biswajeet Pradhan
Abstract The present study analyses the spatial patterns of historical/present
landslide inventory in the Kuala Lumpur and vicinity areas. The main objective is
to statistically test the spatial nature pattern of landslide inventory, i.e. to deter-
mine whether it rejects the independency of spatial pattern or not (i.e. random or
cluster distribution). For that purpose, the nearest neighbor index (NNI) was
applied to measure and test the randomness. First, we tested the spatial patterns of
153 landslides. The results showed a percentage of clustered to dispersed was
85 % (130 events) to 15 % (23 events), indicating landslides have a cluster pattern
tendency. Then, the spatial relationship between the cluster landslides and con-
ditioning factors were analyzed using evidential belief function (EBF) model.
Additionally, the susceptible map produced by an earlier study was used to
compare the results of the inventory selection. Finally, two landslide susceptible
maps (LSMs) were validated by using prediction rate curve techniques. Prediction
accuracy of the cluster data LSM2 was 0.80 (80 %), whereas the random data
produced LSM1 showed 0.75 (75 %) prediction accuracy. From the results
obtained in this study, one can infer that the spatial nature pattern of landslide
inventory follows a cluster patterns. Secondly, clustered data can be used as
training data instead of random selection technique. As a conclusion, the same
technique can be replicated elsewhere.
Keywords Landslide
Spatial pattern analysis
Cluster
Nearest neighbor
index
GIS
Malaysia
