基于GRU 模型的人工智能分析法在滑坡预测领域
的研究及应用
滑坡作为山地与库区地区最为频繁且危害严重的地质灾害之一，其演化受地质条件、降雨入渗、库水位消涨及人为扰动等多种因素耦合控制。传统的统计回归与静态机器学习方法难以有效捕捉台阶式、非平稳的位移特征。近年来，深度循环神经网络在滑坡预测中展现了良好的建模能力，其中门控循环单元（GRU）以参数少、训练快、收敛稳定等优势，在工程部署中具有应用潜力。本文提出“趋势— 周期分解 + GR U 预测”的方法框架，并结合福建泉州梧垵溪与重庆二道河两处典型滑坡案例开展应用验证。结果表明，该方法在趋势项拟合上R² ≥ 0.995，周期项预测的RMSE约为1.7–3.4 mm，MAPE 控制在8%–12%，优于SVM 与部分集成模型。研究表明，基于GRU 的预测框架能够显著提升地质灾害预警的稳定性与可解释性，为库区与河道重点工程的安全运行提供了支撑。
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