人工智能赋能地质灾害风险预测：基于CiteSpace的知识图谱分析

Artificial Intelligence-Enabled Geological Disaster Risk Prediction: Knowledge Map Analysis Based on CiteSpace

摘要: 随着人工智能技术的快速发展，地质灾害风险预测领域正经历从传统方法到数据驱动模型的转变。本文基于CiteSpace软件对2002年10月至2025年10月间在CNKI与Web of Science数据库中以“地质灾害”与“人工智能/机器学习”为主题的文献进行了可视化计量分析，系统揭示了人工智能技术在地质灾害研究中的发展脉络与前沿趋势。研究发现，相关文献自2018年以来显著增长，人工智能技术的应用逐步进入快速发展阶段，研究热点主要集中在“滑坡易发性评价”“机器学习模型优化”和“遥感与多源数据融合”等方向。深度学习、随机森林、支持向量机等算法成为主流技术，推动风险评估从定性分析向定量预测、从经验方法向智能化决策转型。未来，结合可解释性人工智能(XAI)与多源异构数据的融合分析，将是提升地质灾害风险预测精度与科学性的关键。本文为学者深入理解地质灾害智能化研究的演化路径与发展趋势提供了有益参考。

Abstract: With the rapid development of artificial intelligence technology, the field of geological disaster risk prediction is undergoing a transition from traditional methods to data-driven models. Based on CiteSpace software, this paper makes a visual quantitative analysis of the literature on “geological disasters” and “artificial intelligence/machine learning” in CNKI and Web of Science databases from October 2002 to October 2025, and systematically reveals the development context and frontier trend of artificial intelligence technology in geological disaster research. It is found that the relevant literature has increased significantly since 2018, and the application of artificial intelligence technology has gradually entered a stage of rapid development. The research hotspots mainly focus on “landslide susceptibility evaluation”, “machine learning model optimization” and “remote sensing and multi-source data fusion”. Deep learning, random forest, support vector machine and other algorithms have become mainstream technologies, which promote the transformation of risk assessment from qualitative analysis to quantitative prediction, from empirical methods to intelligent decision-making. In the future, the combination of interpretable artificial intelligence (XAI) and multi-source heterogeneous data fusion analysis will be the key to improving the accuracy and scientificity of geological disaster risk prediction. This paper provides a useful reference for scholars to deeply understand the evolution path and development trend of intelligent research on geological disasters.