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An Alternative Technique for Landslide Inventory Modeling Based on Spatial Pattern Characterization

  • Omar F. Althuwaynee
  • Biswajeet PradhanEmail author
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

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 determine 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 conditioning 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 

Notes

Acknowledgments

The authors gratefully acknowledge the financial support from the UPM-RUGS project grant, vote number: 9344100 with additional support from FIG grant.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of EngineeringUniversity Putra MalaysiaSerdangMalaysia

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