摘要
针对现有城市地面沉降预测方法过度依赖沉降数据、模型单一等问题,以云南省昆明市主城区为研究对象,从多时序多因子角度提出一种改进BP神经网络在城市地面沉降中的预测方法。首先,利用SBAS-InSAR技术获取主城区地面沉降监测值,然后通过SPSSAU软件中的灰色关联分析和因子分析选取主城区地面沉降的影响因子,并将其与获取的沉降监测值从多因子多时序角度构建GA-BP和PSO-BP预测模型,最后,得出最优的预测模型并进行预测性能验证。实验结果表明:利用SBAS-InSAR能有效监测城市地面沉降;GA-BP算法相比PSO-BP算法在城市地面沉降预测中性能更好、精度更高;该方法可对长时间、大范围城市地面沉降预测和对某一沉降点多期沉降趋势进行预测。该方法可作为城市地面沉降预测的有效手段,为政府部门决策提供了一种高效快速的方法。
In response to the issues of excessive reliance on subsidence data and a lack of model diversity in existing urban ground subsidence prediction methods,this study focuses on the main urban area of Kunming City,Yunnan Province.A novel approach for predicting urban ground settlement is proposed,incorporating a multi-temporal sequence and multifactor perspective into the improved BP neural network.Firstly,SBAS-InSAR technology is utilized to acquire monitoring values of ground settlement in the main urban area.Subsequently,gray correlation analysis and factor analysis in SPSSAU software are employed to identify the influencing factors of ground settlement in this specific area.Based on the obtained settlement monitoring values and the identified influencing factors,GA-BP and PSO-BP prediction models are constructed from a multifactor multi-temporal sequence viewpoint.The optimal prediction model is determined and its performance is evaluated through comprehensive validation.Experimental results demonstrate that SBAS-InSAR effectively monitors urban ground settlement,while the GA-BP algorithm outperforms the PSO-BP algorithm in terms of prediction accuracy and overall performance.This method allows for long-term and large-scale predictions of urban ground settlement,as well as forecasting the settlement trends of specific points over multiple periods.Consequently,it serves as an effective tool for urban ground settlement prediction,providing governmental departments with an efficient and fast decision-making approach.
作者
周定义
左小清
赵志芳
喜文飞
葛楚
ZHOU Dingyi;ZUO Xiaoqing;ZHAO Zhifang;XI Wenfei;GE Chu(Institute of International Rivers and Eco-Security,Yunnan University,Kunming 650050,Yunnan,China;School of Land Resource Engineering,Kunming University of Science and Technology,Kunming 650093,Yunnan,China;School of Earth Sciences,Yunnan University,Kunming 650050,Yunnan,China;Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization,MNR,Kunming 650051,Yunnan,China;Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization,Kunming 650051,Yunnan,China;Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering,Kunming 650500,Yunnan,China;Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring,Kunming 650051,Yunnan,China;Kunming Urban Planning&Design Institute Co.,Ltd.,Kunming 650041,Yunnan,China)
出处
《地质通报》
CAS
CSCD
北大核心
2023年第10期1774-1783,共10页
Geological Bulletin of China
基金
国家自然科学基金项目《基于张量分解的分布式目标InSAR相位估计与形变模型解算》(批准号:42161067)
云南省应用基础研究计划面上项目《基于全卷积神经网络的多源遥感影像变化检测》(编号:2018FB078)
云南省教育厅科学研究基金项目《顾及InSAR监测适宜性并引入形变速率分级的滑坡敏感性评价新方法》(编号:2023Y0196)。
