Database of Environmental Factors–Driven Machine Learning for Global Landslide Susceptibility

This is the relevant data for the paper titled Machine Learning-Driven Global Landslide Susceptibility Assessment Reveals Critical Roles of Environmental Dynamics.The data mainly includes machine learning correlation factors, calculation results, final picture results, and relevant machine learning code. Due to the limitation of the number of files, they are packaged as compressed files for uploading. Below, I will introduce the relevant documents.

ML factors.rar:The file contains all the input files for training machine learning with 17 factors and includes historical landslide data from around the world.

ML result.rar:This file is the result file of machine learning, which mainly includes the results of three runs. Normal is calculated using normal data. Winter is to replace the rainfall and NDVI data with winter, and summer is to replace the rainfall and NDVI data with summer. The file not only has these results, but also some analysis files, including the standard deviations of the normal seven models and the greater than less than calculation file after the model is subtracted, and the standard deviation file for the seven models. There are also files after summer and winter are subtracted.

huapoyifa2-14-new.py:This file is the code used for machine learning.

figures.rar:This file contains the image drawn by the final analysis results, which is a display of data analytics.