Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Abstract Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) particle swarm (PSO) and water cycle (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), support vector regression (SVR), nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, convergence. results obtained Shuping Baishuihe landslides demonstrate that can be utilized determine optimum hyperparameters present statistical significance, thus enhancing accuracy reliability ML-based Significant differences observed among metaheuristics. Based on test, which was performed root mean square error (RMSE), Kling-Gupta efficiency (KGE), PSO recommended hyperparameter tuning SVR-based prediction due its ability maintain balance between precision, robustness. promising presenting