Susceptibility assessment of precipitation-induced mass landslides based on optimal random forest model: Taking the extreme precipitation event in western Qinling mountains as an example

Random forest model (RF) is one of the widely used machine learning models for landslide susceptibility assessment. Aiming at the difficult problems that restrict the application quality of random forest model assessment, taking more than 20000 extreme rainfall landslides induced by extreme rainfall in Niangniangba Town, western Qinling Mountains as an example, the model optimization and comparison with conventional model evaluation were carried out mainly from four aspects: landslide−non−landslide sample screening method, influence factor selection, coupling method application and hyper−parameter optimization. Based on the above optimization, the regional landslide susceptibility evaluation and effectiveness comparison of typical towns−Niangniangba Town are carried out. The evaluation of both situations has achieved ideal results. The optimized random forest evaluation result AUC can reach 0.877, which is better than the conventional assessment results. It shows that the optimization method can obviously improve the assessment effect and learning efficiency of random forest model in regional rainfall landslide, and can provide reference for the risk assessment of extreme rainfall landslide hazard under the background of climate change.