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Official Journal of the Asia Oceania Geosciences Society (AOGS)

Table 2 The information and data source of landslide predisposing factors

From: Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping

Landslide predisposing factors

Original format

Resolution

Classification method

Data source

Altitude (m)

Grid

30 m \(\times\) 30 m

natural break (Jenks)

Extracting from DEM image (http://www.gscloud.cn/)

Slope angle (\(^\circ\))

Grid

30 m \(\times\) 30 m

natural break (Jenks)

Extracting from DEM image (http://www.gscloud.cn/)

Slope aspect

Grid

30 m \(\times\) 30 m

natural break (Jenks)

Extracting from DEM image (http://www.gscloud.cn/)

NDVI

Grid

30 m \(\times\) 30 m

natural break (Jenks)

Generating by GF-2 remote sensing images obtained from Xi'an Satellite Measurement and Control Center

Distance to rivers (m)

Vector

30 m \(\times\) 30 m

Equal interval

Generating by regional water system obtained from the local government

Distance to roads (m)

Vector

30 m \(\times\) 30 m

Equal interval

Generating by regional traffic maps obtained from the local government

Distance to faults (m)

Vector

30 m \(\times\) 30 m

Equal interval

Extracting from geological maps with 1:500,000 scale obtained from the local government

MAP (mm/year)

Vector

30 m \(\times\) 30 m

Equal interval

Extracting from rainfall observation data from 2010 to 2021 obtained from the local government

Lithology

Vector

30 m \(\times\) 30 m

Custom interval

Extracting from geological maps with 1:500,000 scale obtained from the local government