知识–数据–机理三元融合驱动的滑坡智能预测研究进展

Research Progress on Intelligent Landslide Prediction Driven by the Integration of Knowledge, Data, and Mechanisms

摘要: 滑坡灾害因其突发性、隐蔽性与强破坏性对人类社会构成严重威胁，传统预测方法在极端工况下存在预警时效性与精度不足的局限，而纯数据驱动的深度学习模型因物理机制脱节与可解释性缺失，面临样本依赖性高及跨场景泛化能力弱的核心瓶颈。为此，本文提出“知识–数据–机理”三元融合驱动框架，通过动态闭环耦合机制整合地质力学先验知识(如岩土蠕变本构方程、历史滑坡判识准则)、空–天–地–深立体观测数据(InSAR形变序列、微震信号)及跨尺度演化机理(微观孔隙水压扩散方程→宏观位移加速突变准则)，突破滑坡智能预测的泛化瓶颈。研究表明：知识层为模型构建提供物理约束边界(如将Saito蠕变方程嵌入LSTM损失函数使误报率降低40%)，机理层保障数据挖掘的物理一致性(如渗流定律规范GAN生成样本力学参数)，数据层驱动机理参数反演(如InSAR校准岩体渗透系数)。系统综述揭示现有模型在三元融合维度的结构性缺陷(如U-Net++因忽略地形–岩性互馈机制导致古老滑坡漏检率31%)，并指出未来需通过知识图谱化(构建滑坡演化知识图谱)、机理可微化(采用PINNs将非饱和渗流方程转化为可导损失项融入Transformer)及神经符号融合(如InterTris模型实现92.7%的因果推理准确率)等技术路径，实现滑坡预测从数据拟合向机理驱动范式的跃迁。该框架为发展灾害风险主动防控新范式提供理论支撑，服务国家重大工程安全战略需求。

Abstract: Landslide disasters pose a severe threat to human society due to their suddenness, concealment, and strong destructiveness. Traditional prediction methods have limitations in early warning timeliness and accuracy under extreme conditions, while purely data-driven deep learning models face core bottlenecks such as high sample dependence and weak cross-scenario generalization due to disconnection from physical mechanisms and lack of interpretability. To address this, this study proposes a “knowledge-data-mechanism” tripartite fusion-driven framework. It integrates geological mechanics prior knowledge (e.g., rock-soil creep constitutive equations, historical landslide identification criteria), space-air-ground-deep multi-dimensional observation data (e.g., InSAR deformation sequences, microseismic signals), and cross-scale evolution mechanisms (e.g., from microscopic pore water pressure diffusion equations to macroscopic displacement acceleration mutation criteria) through a dynamic closed-loop coupling mechanism, breaking through the generalization bottleneck of intelligent landslide prediction. Research shows that the knowledge layer provides physical constraint boundaries for model construction (e.g., embedding the Saito creep equation into the LSTM loss function reduces the false alarm rate by 40%); the mechanism layer ensures the physical consistency of data mining (e.g., seepage laws regulate the mechanical parameters of GAN-generated samples); and the data layer drives the inversion of mechanism parameters (e.g., InSAR calibrates rock mass permeability coefficients). A systematic review reveals structural defects of existing models in the dimension of tripartite fusion (e.g., U-Net++ ignores terrain-lithology feedback mechanisms, leading to a 31% missed detection rate for ancient landslides). It further points out that future efforts should focus on technical paths such as knowledge graph construction (building landslide evolution knowledge graphs), mechanism differentiability (using PINNs to convert unsaturated seepage equations into differentiable loss terms integrated into Transformer), and neuro-symbolic fusion (e.g., the InterTris model achieves a 92.7% causal reasoning accuracy), to realize the paradigm shift of landslide prediction from data fitting to mechanism-driven. This framework provides theoretical support for developing a new paradigm of active disaster risk prevention and control, serving the strategic needs of national major engineering safety.