Open Access

ARTICLE

Novel Sine-Lucas Oscillation Particle Swarm Optimization XGBoost Method for Landslide Prediction

Qisen Jin^1,2, Xiaoping Wang¹, Feng Zhang², Yu Zeng², Jia Guo^3,4,5,*, Jiacheng Li^6,*

1 Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
2 China Communications Second Highway Consultants Co., Ltd., Wuhan, China
3 Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, China
4 Faculty of Information Science and Technology, Wenhua College, Wuhan, China
5 Faculty of Computer and Information Science, Hosei University, Tokyo, Japan
6 Department of Applied Mathematics and Systems, Faculty of Informatics, Kanagawa University, Yokohama, Japan

* Corresponding Authors: Jia Guo. Email: ; Jiacheng Li. Email:

(This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)

Computers, Materials & Continua 2026, 89(1), 45 https://doi.org/10.32604/cmc.2026.080077

Received 02 February 2026; Accepted 18 June 2026; Issue published 13 August 2026

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.

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Keywords

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