Spatial and temporal prediction of ground subsidence in mining areas considering seasonal characteristics
Mining can cause severe ground subsidence,which is frequently accompanied by widespread and uneven characteristics,posing considerable threats to production and life in a mining community.The timely and accurate monitoring and prediction of ground subsidence in mining areas are crucial for mitigating its adverse effects.However,traditional spatiotemporal prediction models for ground subsidence often experience difficulty in capturing comprehensive spatiotemporal information and learning the intricate features associated with this phenomenon.To address these challenges,this study incorporates a temporal decomposition strategy into a deep learning network model,resulting in the development of the Spatiotemporal Forecasting Framework(SFF-PredRNN)model.This innovative approach considers seasonal displacement features,enhancing the model's ability to accurately capture complex spatiotemporal patterns.By integrating this advanced methodology,the SFF-PredRNN model offers improved predictive capabilities,allowing for effective mitigation measures against ground subsidence and its associated risks.The focus of this study is the Micun coal mine located in Xinmi City,a region characterized by extensive mineral resource extraction and distinct seasonal variations in rainfall.The summer season contributes significantly to the annual rainfall,accounting for 60.9％.Certain mining areas within this region have experienced notable ground subsidence issues.By using the small baseline set interference technique algorithm,ground subsidence data from 2018 to 2021 were collected for the study area.The analysis revealed distinct spatial differences in subsidence patterns,particularly in the Mengzhuang and Zhangpocun coal mines at the center and the Wangzhuang coal mine in the southwest.These areas exhibited severe ground subsidence problems,with the maximum subsidence reaching 256 mm,while the surrounding regions did not experience significant ground subsidence.A spatiotemporal dataset of ground subsidence was constructed based on the collected information,and the developed SFF-PredRNN model was employed for prediction.The model's accuracy was assessed using metrics,such as mean absolute error,root mean square deviation,peak signal-to-noise ratio,and structural similarity index measure.Meanwhile,to assist in verifying the advantages of the model in the spatiotemporal prediction of the mine area,we selected a profile line that crossed the mine area in the horizontal and vertical directions and chose equal spacing to take out a certain number of subsidence points.Then,we extracted the subsidence values predicted by the model through these points and verified the results.The results demonstrated that the SFF-PredRNN model,as proposed in this study,exhibited superior accuracy in predicting subsidence for the years 2019,2020,and 2021.This finding highlights the model's strengths in the temporal and spatial predictions of ground subsidence.The predictions for the upcoming year indicated a continued trend of subsidence in the mining areas of Mengzhuang,Wangzhuang,and Zhangpocun,with an expected maximum cumulative subsidence of 274.3 mm.The spatial distribution of settlement in the study area remained consistent with previous patterns.In conclusion,the SFF-PredRNN model proposed in this study exhibits good performance in the spatiotemporal prediction of ground subsidence,and thus,it can be used as an effective method for the spatiotemporal prediction of ground subsidence.This study provides effective methodological guidance for the prevention and early warning of ground subsidence disasters in mining areas.In the future,we can improve the prediction model by integrating more data on ground subsidence influencing factors to realize more accurate spatiotemporal prediction on a large scale.
万方数据
维普
