, Volume 14, Issue 1, pp 1–17 | Cite as

A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

  • Dieu Tien BuiEmail author
  • Quoc Phi Nguyen
  • Nhat-Duc Hoang
  • Harald Klempe
Original Paper


This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC = 0.944) and validation dataset (AUC = 0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.


Landslide Fuzzy k-nearest neighbor Differential evolution GIS Quy Hop Vietnam 



This research was supported by the project B2014-02-21 (Hanoi University of Mining and Geology, Vietnam) and was partially supported by University College of Southeast Norway, Bø i Telemark, Norway.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Dieu Tien Bui
    • 1
    • 2
    Email author
  • Quoc Phi Nguyen
    • 3
  • Nhat-Duc Hoang
    • 4
  • Harald Klempe
    • 1
  1. 1.Geographic Information System Group, Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBø i TelemarkNorway
  2. 2.Faculty of Geomatics and Land AdministrationHanoi University of Mining and GeologyHanoiVietnam
  3. 3.Department of Environmental SciencesHanoi University of Mining and GeologyHanoiVietnam
  4. 4.Faculty of Civil Engineering, Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

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