基于随机森林算法的天津市滨海地区地面沉降模拟

Investigations into ground subsidence in Tianjin coastal area based on random forest

地面沉降的监测与预测,对于保障城市安全和社会可持续发展具有重要意义和现实价值。利用随机森林机器学习模型预测了天津市滨海地区的地面沉降量空间分布,并评估了模型的性能和变量的重要性。基于2020年天津市滨海新区局部地区的地面沉降量、含水层岩性、含水组水位差、水文地质参数等数据来训练和验证随机森林模型。结果表明:随机森林模型能够较好地拟合和预测地面沉降量(R^(2)=0.98,RMSE=0.52 mm);影响地面沉降量最重要的因素是水位差,其次是含水层的岩性(砂-黏比值),最后是相关水文地质参数。上述结果与传统上太沙基原理基本吻合,进一步验证了模型的正确性和可预测性。本研究采用了空间分布数据来训练随机森林模型;根据物理机制,选取了重要控制因素来开展分析;评估了控制因素的重要性程度,并指出了研究的局限性和未来的研究方向,为利用随机森林模型快速有效预测地面沉降提供了重要参考和启示。

[Objective]The spatial distribution of ground subsidence in the coastal area of Tianjin was predicted using a random forest machine learning model,in which the performance and significance of the variables were evaluated.[Methods]The random forest model was trained and validated in this study using datasets of ground subsidence in 2020,aquifer lithology,water level differences in aquifers in 2020,and hydrogeological parameters.[Results]The results demonstrate the effectiveness of the random forest model for fitting and predicting ground subsidence(R^(2)=0.98,RMSE=0.52 mm).Moreover,it is found that water level difference emerges as the most influential factor affecting ground subsidence,followed by lithology and hydrogeological parameters.[Conclusion]The present study introduces several novel contributions:①utilization of spatial distribution data for training ground subsidence models;②identification of significant controlling factors based on physical mechanisms;③assessment of the relative importance of these controlling factors.Additionally,this paper highlights the limitations and future directions in ground subsidence research,offering valuable insights for the rapid and accurate prediction of ground subsidence using the random forest model.