融合地面沉降因素的沿海城市生态安全格局识别——基于深度学习的珠海市案例研究  

Coastal Urban Ecological Security Pattern Identification Integrating Land Subsidence Factors:A Deep Learning-Based Case Study of Zhuhai City

作　　者：袁少雄 宫清华 叶玉瑶[2] 王钧 郝银磊 张雅泽 刘博文 Yuan Shaoxiong;Gong Qinghua;Ye Yuyao;Wang Jun;Hao Yinlei;Zhang Yaze;Liu Bowen(Guangdong Academy of Sciences Zhuhai Industrial Technology Research Institute Co.,Ltd.,Zhuhai 519090,China;Guangzhou Institute of Geography,Guangdong Academy of Sciences//Guangdong Open Laboratory of Geospatial Information Technology and Application,Guangzhou 510070,China)

机构地区：[1]广东省科学院珠海产业技术研究院有限公司,广东珠海519090 [2]广东省科学院广州地理研究所//广东省地理空间信息技术与应用公共实验室,广州510070

出　　处：《热带地理》2025年第4期673-690,共18页Tropical Geography

基　　金：珠海市社会发展领域科技计划项目(2320004000189);国家基金面上项目(42271091);广东省自然科学基金项目(2024A1515030114)。

摘　　要：快速城市化与地质灾害频发对区域生态安全构成挑战。传统生态安全格局(ESP)构建方法较少考虑地面沉降等垂直地质因素,这可能导致沿海城市生态功能区划不合理和生态系统服务功能的降低。文章以珠海市为例,探索了地面沉降因素对生态安全格局构建的影响机制。采用多层感知器(MLP)深度学习模型进行ESP预测,结合加权平均、非线性融合、信息熵和主成分分析等多源数据融合方法进行格局分类和效果评估。结果显示,MLP模型的平均预测准确率达84.5%。空间分析揭示了地面沉降对ESP的影响存在显著空间异质性,中等历史沉降区(8~41 mm/a)表现出最显著影响。源地区和建设区域分别有7.14%和9.84%的区域表现为轻微沉降(2~8 mm/a),应作为重点监测与管理区域。不同融合方法在识别特定功能区域方面表现各异:主成分分析(前2个主成分分别解释了27.1%和19.8%的方差)和信息熵方法在识别建设区和廊道区方面表现优异,而非线性融合在源地区识别方面具有优势。通过整合地面沉降监测数据和多源数据融合方法,文章为沿海城市ESP优化提供了方法学参考,辅助识别了以沿海湿地和河口系统为核心的珠海市生态安全格局。研究表明,在地面沉降约束下协调生态保护、灾害防治与城市发展是可行的。未来研究应重点关注高分辨率时空数据的应用、算法优化,以及研究成果向城市规划和生态管理政策的高效转化机制。Rapid urbanization and geological disasters pose significant challenges to regional ecological security.Although Ecological Security Pattern(ESP)construction is important for ecosystem stability and sustainable development,traditional approaches rarely incorporate vertical geological factors,such as land subsidence.This study proposes a framework that integrates land subsidence into ESP construction through machine learning and multi-source data fusion methods.Using Zhuhai City as a case study,we analyzed 30 environmental variables,including historical land subsidence data,topography,soil distribution,land use,climatic factors,and human activity indicators.The methodology consisted of four main steps:(1)correlation and principal component analyses to identify key factors and reduce dimensionality;(2)development of a multilayer perceptron(MLP)deep learning model with three fully connected hidden layers using ReLU activation functions and dropout regularization to predict ecological pattern types;(3)comparison of four fusion methods(weighted average,nonlinear sigmoid transformation,information entropy,and principal component analysis)to integrate prediction results;and(4)spatial analysis of the relationship between land subsidence and ecological security patterns using chi-square tests and spatial overlay analysis.Results showed that the MLP model achieved an average prediction accuracy of 84.5%with an F1-score of 0.844,demonstrating the feasibility of deep learning approaches in ESP construction.The principal component analysis showed that the first four principal components cumulatively explained 71.4%of the total variance,with the first two components explaining 27.1%and 19.8%.The first principal component was dominated by climatic factors,whereas the second primarily reflected the topographic and geological vulnerability characteristics.Spatial analysis revealed significant spatial heterogeneity in the impact of land subsidence on the ESP,with moderate historical subsidence(8-41 mm/year)showing more notable eff

关 键 词：生态安全格局 深度学习 地面沉降 多源数据融合 空间异质性 珠海市 

分 类 号：X321[环境科学与工程—环境工程]