Research on the application of Breiman's algorithm integrated with the Random Forest in determining the importance of input factors to landslide formation in Son La province

Abstract

Landslide susceptibility maps are effective and intuitive tools in natural
disaster management, helping to minimize damage caused by natural
disasters due to the specific spatial information they provide. However, the
performance of these susceptibility maps depends mainly on the number
and importance of input factors. Determining the importance and order
of influencing factors often receives little attention in landslide prediction
studies. Breiman's algorithm, integrated into the Random Forest method,
can comprehensively determine the importance and order of input
variables by considering the correlation relationship between the
landslide inventory map and these input variables. Consequently, this
study utilized Breiman's algorithm within the Random Forest technique
to assess the importance of 16 input factors influencing the formation of
landslide events in Son La province. The results obtained from this study
serve as the foundation for selecting appropriate input factors to enhance
the construction and accuracy of landslide susceptibility maps within the
study area, especially in the context of climate change