Landslide susceptibility mapping of Phewa catchment using multilayer perceptron artificial neural network

Authors

  • Ananta Man Singh Pradhan Department of Ocean Engineering, Geosystems Engineering Laboratory, Pukyong National University, KOREA
  • Yun-Tae Kim Department of Ocean Engineering, Geosystems Engineering Laboratory, Pukyong National University, KOREA

DOI:

https://doi.org/10.3126/njes.v4i0.22718

Keywords:

Artificial neural network, GIS, Landslide susceptibility

Abstract

The aim of this study was to prepare landslide susceptibility mapping technique using multilayer perceptron artificial neural network (MLP-ANN) and then to apply this method to Phewa catchment in western Nepal. To determine the effect of causative factors on landslides, data layers of aspect, elevation, slope, internal relief, slope shape, drainage proximity, drainage density, stream power index, topographic wetness index, sediment transport index, land cover and geology were analysed in R-statistical package and final map was produced using geographical information system environment. A GIS-based landslide inventory map of 88 landslide locations was prepared using data from previous reports and satellite image interpretation. A MLP-ANN model was generated from a training set consisting of ~70% randomly selected landslide in the inventory map, with the remaining ~30% landslides used for validation of the susceptibility map. According to analysis, the model had a success rate of 82.1% and the prediction accuracy of 91.4%, indicating a good performance.

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Published

2016-12-05

How to Cite

Pradhan, A. M. S., & Kim, Y.-T. (2016). Landslide susceptibility mapping of Phewa catchment using multilayer perceptron artificial neural network. Nepal Journal of Environmental Science, 4, 1–9. https://doi.org/10.3126/njes.v4i0.22718

Issue

Section

Research Articles