Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models

2016 ◽  
Vol 20 (5) ◽  
pp. 705-718 ◽  
Author(s):  
Qiqing Wang ◽  
Wenping Li ◽  
Maolin Xing ◽  
Yanli Wu ◽  
Yabing Pei ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Artificial Neural

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

2016 ◽  
Vol 4 ◽  
pp. 1-9 ◽  
Author(s):  
Ananta Man Singh Pradhan ◽  
Yun-Tae Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Geographical Information ◽  
Landslide Susceptibility Mapping ◽  
Mapping Technique ◽  
Inventory Map ◽  
Artificial Neural

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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