Xiao Yixin, Tao Qiuxiang, Hu Leyin, Liu Ruixiang, Li Xuepeng
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266000, China.
Demonstration Center for Experimental Surveying and Mapping Education, Shandong University of Science and Technology, Qingdao, 266000, China.
Sci Rep. 2024 Aug 19;14(1):19139. doi: 10.1038/s41598-024-70115-0.
In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error.
在煤矿开采区，地表沉陷对人类生命和财产构成重大风险。幸运的是，通过使用各种方法，如概率积分法和深度学习（DL）方法，可以对煤矿开采引起的地表沉陷进行监测和预测。尽管DL方法在预测沉陷方面显示出前景，但由于对空间相关性和时间非线性考虑不足，它们往往缺乏准确性。考虑到这一问题，我们提出了一种基于DL的新颖方法来预测开采地表沉陷。我们的方法采用K均值聚类对空间数据进行划分，从而能够应用门控循环单元（GRU）模型来捕捉每个分区内沉陷时间序列中的非线性关系。使用蛇优化（SO）进行优化进一步在全局上提高了模型的准确性。验证表明，我们的方法优于传统的长短期记忆（LSTM）和GRU模型，在99.1%的样本像素上实现了绝对误差小于8毫米的效果。
