@Article{cmc.2026.080077,
AUTHOR = {Qisen Jin, Xiaoping Wang, Feng Zhang, Yu Zeng, Jia Guo, Jiacheng Li},
TITLE = {Novel Sine-Lucas Oscillation Particle Swarm Optimization XGBoost Method for Landslide Prediction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27578},
ISSN = {1546-2226},
ABSTRACT = {This paper presents a Sine-Lucas Oscillation Particle Swarm Optimization (SLOPSO) XGBoost algorithm for landslide susceptibility prediction. SLOPSO extends canonical PSO by introducing an oscillation factor constructed from a sine function and the Lucas number sequence into the position update, which raises swarm diversity and improves the global search. SLOPSO is then applied to tune six XGBoost hyperparameters using K-fold cross-validation accuracy as the fitness function. In experimental evaluation, SLOPSO-XGBoost reaches a mean test Accuracy of 0.8113, F1 of 0.8182, and AUC of 0.8856 over 10 independent runs, outperforming standard PSO-XGBoost and the default Random Forest, SVM, and XGBoost baselines on every metric. The experimental results demonstrate that SLOPSO is capable of automatically tuning the hyperparameters of XGBoost, and that SLOPSO-XGBoost able to provide high precision solution for landslide event prediction.},
DOI = {10.32604/cmc.2026.080077}
}