Comparison of Statistical and Machine-Learning Model for Analyzing Landslide Susceptibility in Sumedang Area, Indonesia

Landslides have produced several recurrent dangers, including losses of life
and property, losses of agricultural land, erosion, population relocation, and
others. Landslide mitigation is critical since population and economic expan
sion are rapidly followed by significant infrastructure development, increas
ing the risk of catastrophes. At an early stage in landslide-disaster mitigation,
landslide- risk mapping must give critical information to help policies limit the
potential for landslide damage. This study will utilize the comparative frequen
cy ratio (FR) and random forest (RF) techniques; they will be utilized to proper
ly investigate the distribution of flood vulnerability in the Sumedang area. This
study has identified 12 criteria for developing a landslide- susceptibility model
in the research region based on the features of past disasters in the research area.
The FR and RF models scored 88 and 81% of the AUC value, respectively. Based
on the McNemar test, the FR and RF models featured the same performance
in determining the landslide-vulnerability level performances in Sumedang.
They performed well in assessing landslides in the research region; therefore,
they may be used as references in landslide prevention and ref er ences in future
regional development plans by the stakeholders.