一种地理加权随机森林算法的城市沉降模式识别与预测  

Urban subsidence pattern recognition and prediction using a geographically weighted random forest algorithm

作　　者：胡文西 闫石 HU Wenxi;YAN Shi(Henan Sixth Geological Team Co.,Ltd.,Zhengzhou 450018,China)

机构地区：[1]河南省第六地质大队有限公司,郑州450018

出　　处：《时空信息学报》2025年第1期104-112,共9页JOURNAL OF SPATIO-TEMPORAL INFORMATION

摘　　要：随着全球城市化进程的加速,地面沉降对基础设施安全和环境稳定构成了严重威胁,尤其在经济快速发展的城市中,负面影响愈加显著;目前研究主要侧重于提升沉降监测的空间覆盖率或仅针对观测点进行沉降预测,未将两者结合考虑,从而限制了对区域性沉降模式的全面识别与精准预测。因此,本文以武汉市某大型小区施工现场为研究对象,提出一种采用地理加权的随机森林算法,结合2023年4月16~7月29日的三期沉降监测数据,识别研究对象内74个沉降监测点的沉降模式,并进行预测给出预警方案;为验证方法有效性,与已有方法克里金插值法、随机森林算法进行比较分析。结果表明:在沉降严重时期,相较于其他两种方法,本文方法均方根误差降低了25%、平均绝对误差降低了30%,识别效果最好;结合预测未来30 d的沉降模式,得出预警方案,即需要加固东部和北部建筑群,以避免沉降的进一步加重。[Objective]Urban subsidence poses a significant threat to infrastructure and environmental stability,particularly in rapidly growing cities.Traditional monitoring methods struggle to accurately capture complex,nonlinear subsidence patterns.This study introduces the geographically weighted random forest(GWRF)approach,integrating multi-period subsidence monitoring data to enhance pattern recognition and prediction.The research analyzes subsidence trends at 74 monitoring points within a construction site from April 16 to July 29,2023.[Method]To overcome the limitations of conventional techniques,this study applies the GWRF method,which incorporates spatially adaptive weighting to account for regional subsidence variations.Unlike Kriging interpolation and traditional random forest methods,the GWRF dynamically adjusts prediction weights based on local spatial features,leading to more precise subsidence forecasting.The performance of GWRF,Kriging,and random forest is compared using root mean squared error(RMSE)and mean absolute error(MAE)as evaluation metrics.[Result]In contrast to traditional random forest,which fails to account for spatial variation,the GWRF reduces prediction bias and enhances the accuracy of subsidence forecasts.The results suggest that during the monitoring period,the most significant subsidence occurred in the road surface and northwest building areas,influenced by construction activities.The findings show that GWRF outperforms both Kriging and random forest in identifying and predicting subsidence trends.Compared to the other methods,the GWRF reduces RMSE by 25%and MAE by 30%,particularly during periods of severe subsidence.The model also provides reliable forecasts for future subsidence trends,highlighting areas in the eastern and northern construction zones that require reinforcement to mitigate risks.Between April 16 and June 11,2023,maximum subsidence reached-12 mm in the road surface and northwest building areas,primarily due to road construction and underground pipeline installation.From J

关 键 词：随机森林算法 克里金插值法 地理加权模型 城市沉降 沉降预测 沉降模式识别 

分 类 号：P642.26[天文地球—工程地质学]