Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility.

ABSTRACT: In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of regression models. The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past. The study started with 18 potential predisposing factors, which were reduced to 14 after a multi-approach feature selection technique. Six susceptibility models were implemented and compared usi

SUBMITTER: Ajin RS 

PROVIDER: S-EPMC11496660 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature