Assessment of urban ground collapse susceptibility based on RF-BP neural network coupling model: A case study of typical areas in Hangzhou City
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摘要:
为了改变地面塌陷易发性评价主要通过知识驱动模型实现的现状，文章探讨了将数据驱动模型引入城市地面塌陷评价的可能性，选取杭州市填土-粉砂土典型区域为研究区，进行了研究区地面塌陷指标因子的选择以及相关性检验，筛选出了排水管线密度、社会活动密度、地下承压水位埋深、表层填土层厚度、与暗河暗浜距离、饱和砂土顶板埋深、软土层厚度7个评价因子对研究区地面塌陷易发性进行了评价，通过对比RF、I-RF集成模型、RF-BP神经网络模型，得到了在该研究区背景下集成模型相对单模型对地面塌陷易发性评价结果精确度更高，最后选取了效果最好的RF-BP神经网络集成模型进行了易发性评价。评价结果显示：易发性分区与地面塌陷隐患区高度吻合，预测效果较好，证明了数据驱动模型在城市地面塌陷易发性评价方面应用的可能性。
Abstract:To improve the current situation where ground subsidence susceptibility assessment mainly relies on knowledge-driven models, this study explores the feasibility of incorporating data-driven models into the evaluation of urban ground subsidence. The study focused on a typical area in Hangzhou characterized by fill and silty soil. The selection of ground collapse indicators was conducted, followed by a correlation test. 7 evaluation factors, including drainage pipeline density, social activity density, depth of underground confined water level, thickness of surface fill layer, distance from hidden rivers and beaches, depth of the saturated sand top plate, and thickness of the soft soil layer, were selected for assessing the susceptibility to ground subsidence in the study area. By comparing the random forest (RF) model, RF-I integrated model, and RF-BP neural network integrated model, it was found that the integrated model had higher accuracy in assessing the susceptibility of ground collapses subsidence in this study area compared to single models. Ultimately, the RF-BP neural network integrated model, which showed the best performance, was chosen for susceptibility assessment. The assessment results indicated a high correlation between the susceptibility zones and areas prone to ground subsidence, indicating good prediction performance and proving the potential application of data-driven models in evaluating the susceptibility of urban ground collapses.
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表 1 研究区部分典型塌陷点垂向分布特征
Table 1. Table of vertical distribution characteristics of some typical collapse points in the study area
位置 塌陷深度/m 填土厚度/m 5～10 m内岩性组合 塌陷示意图 滨江区闻涛路江陵路路口附近塌陷 0.8 4.1 填土-砂质粉土 
滨江区西兴路和丹枫路交叉口塌陷 1.5 0.9 填土-砂质粉土 
滨江区通和路空洞隐患 1.7 1.7 填土-砂质粉土 
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表 2 研究区地面塌陷灾害易发性分区表
Table 2. Susceptibility zoning table for ground collapse disasters in the study area
滑坡灾害
易发性等级分区面积
/km2占总面积比
/%地面塌陷
隐患区数量/个占总灾害
数量比/%低易发 13.97 37.87 0 0 中易发 3.20 8.68 1 4.76 高易发 13.10 35.52 5 23.81 极高易发 6.61 17.93 15 71.43
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[1] 刘伟. 我国地质灾害调查统计与分析[J]. 采矿技术,2021,21(5):100 − 103. [LIU Wei. Investigation,statistics and analysis of geological disasters in China[J]. Mining Technology,2021,21(5):100 − 103. (in Chinese with English abstract)] doi: 10.3969/j.issn.1671-2900.2021.05.029
LIU Wei. Investigation, statistics and analysis of geological disasters in China［J］. Mining Technology, 2021, 21（5）: 100 − 103. （in Chinese with English abstract） doi: 10.3969/j.issn.1671-2900.2021.05.029
[2] 邵泽斐,马青山,葛伟亚,等. 杭州市地面沉降重点区土体压缩和固结特性研究——以试验孔HZ22J06为例[J/OL]. 华东地质, 2024:1 − 10. (2024-10-22). [SHAO Zefei,MA Qingshan,GE Weiya,et al. Study on characteristics of soil compression and consolidation in key areas of land subsidence in Hangzhou:Taking experimental hole HZ22J06 as an example[J/OL]. East China Geology,2024:1 − 10. (2024-10-22). https://link.cnki.net/doi/10.16788/j.hddz.32-1865/P.2023.05.001.]
SHAO Zefei, MA Qingshan, GE Weiya, et al. Study on characteristics of soil compression and consolidation in key areas of land subsidence in Hangzhou: Taking experimental hole HZ22J06 as an example［J/OL］. East China Geology, 2024: 1 − 10. （2024-10-22）. https://link.cnki.net/doi/10.16788/j.hddz.32-1865/P.2023.05.001.
[3] 雷廷, 马青山, 葛伟亚, 等. 地面沉降钻孔全断面光纤多参量精细化监测——以杭州萧山区山末址村为例[J]. 华东地质,2024,45(4):452 − 465. [LEI Ting, MA Qingshan, GE Weiya, et al. Full section multi-parameter precise monitoring of fiber optic in land subsidence borehole: A case study of Shanmozhi Village, Xiaoshan District of Hangzhou[J]. East China Geology,2024,45(4):452 − 465. (in Chinese with English abstract)]
LEI Ting, MA Qingshan, GE Weiya, et al. Full section multi-parameter precise monitoring of fiber optic in land subsidence borehole: A case study of Shanmozhi Village, Xiaoshan District of Hangzhou［J］. East China Geology, 2024, 45（4）: 452 − 465. （in Chinese with English abstract）
[4] 于翠翠,刘兰玉,李传生,等. 基于AHP法的鲁中某高铁岩溶塌陷易发性评价[J]. 山东国土资源,2023,39(2):42 − 49. [YU Cuicui,LIU Lanyu,LI Chuansheng,et al. Karst collapse risks evaluation of a high-speed railway in central Shandong based on AHP method[J]. Shandong Land and Resources,2023,39(2):42 − 49. (in Chinese with English abstract)]
YU Cuicui, LIU Lanyu, LI Chuansheng, et al. Karst collapse risks evaluation of a high-speed railway in central Shandong based on AHP method［J］. Shandong Land and Resources, 2023, 39（2）: 42 − 49. （in Chinese with English abstract）
[5] XIE Yanhua,ZHANG Binghui,LIU Yuxin,et al. Evaluation of the karst collapse susceptibility of subgrade based on the AHP method of ArcGIS and prevention measures:A case study of the Quannan expressway,section K1379+300 - K1471+920[J]. Water,2022,14(9):1432. doi: 10.3390/w14091432
[6] 庄树裕. 基于模糊层次分析法的龙岩市永定区岩溶塌陷灾害易发性评估分析[J]. 福建地质,2022,41(4):323 − 331. [ZHUANG Shuyu. Evaluation of karst collapse disaster susceptibility in Yongding District,Longyan City based on fuzzy hierarchical analysis[J]. Geology of Fujian,2022,41(4):323 − 331. (in Chinese with English abstract)] doi: 10.3969/j.issn.1001-3970.2022.04.008
ZHUANG Shuyu. Evaluation of karst collapse disaster susceptibility in Yongding District, Longyan City based on fuzzy hierarchical analysis［J］. Geology of Fujian, 2022, 41（4）: 323 − 331. （in Chinese with English abstract） doi: 10.3969/j.issn.1001-3970.2022.04.008
[7] 江思义,吴福,黄希明,等. 基于专家-层次分析法的岩溶地面塌陷易发性评价——以广西平桂区为例[J]. 矿产勘查,2021,12(11):2294 − 2302. [JIANG Siyi,WU Fu,HUANG Ximing,et al. Evaluation of vulnerability of karst collapse based on expert-analytic hierarchy process:A case study of Pinggui Distric,Guangxi[J]. Mineral Exploration,2021,12(11):2294 − 2302. (in Chinese with English abstract)] doi: 10.3969/j.issn.1674-7801.2021.11.014
JIANG Siyi, WU Fu, HUANG Ximing, et al. Evaluation of vulnerability of karst collapse based on expert-analytic hierarchy process: A case study of Pinggui Distric, Guangxi［J］. Mineral Exploration, 2021, 12（11）: 2294 − 2302. （in Chinese with English abstract） doi: 10.3969/j.issn.1674-7801.2021.11.014
[8] 王桂林,强壮,曹聪,等. 基于地理探测器与层次分析法的岩溶地面塌陷易发性评价——以重庆中梁山地区为例[J]. 中国岩溶,2022,41(1):79 − 87. [WANG Guilin,QIANG Zhuang,CAO Cong,et al. Evaluation of susceptibility to karst collapse based on the geodetector and analytic hierarchy method:An example of the Zhongliangshan area in Chongqing[J]. Carsologica Sinica,2022,41(1):79 − 87. (in Chinese with English abstract)] doi: 10.11932/karst2021y08
WANG Guilin, QIANG Zhuang, CAO Cong, et al. Evaluation of susceptibility to karst collapse based on the geodetector and analytic hierarchy method: An example of the Zhongliangshan area in Chongqing［J］. Carsologica Sinica, 2022, 41（1）: 79 − 87. （in Chinese with English abstract） doi: 10.11932/karst2021y08
[9] 陈芯宇,师芸,赵侃,等. 基于CF融入SSA优化SVM和RF模型的滑坡易发性评价[J]. 西安理工大学学报,2023,40(1):121 − 131. [CHEN Xinyu, SHI Yun, ZHAO Kan, et al. Landslide susceptibility evaluation based on CF integrated with SSA to optimize SVM and RF models[J]. Journal of Xi’an University of Technology, 2023,40(1): 121 − 131. (in Chinese with English abstract)]
CHEN Xinyu, SHI Yun, ZHAO Kan, et al. Landslide susceptibility evaluation based on CF integrated with SSA to optimize SVM and RF models［J］. Journal of Xi’an University of Technology, 2023, 40（1）: 121 − 131. （in Chinese with English abstract）
[10] COROMINAS J,VAN WESTEN C,FRATTINI P,et al. Recommendations for the quantitative analysis of landslide risk[J]. Bulletin of Engineering Geology and the Environment,2014,73(2):209 − 263.
[11] 陈水满,赵辉龙,许震,等. 基于人工神经网络模型的福建南平市滑坡危险性评价[J]. 中国地质灾害与防治学报,2022,33(2):133 − 140. [CHEN Shuiman,ZHAO Huilong,XU Zhen,et al. Landslide risk assessment in Nanping City based on artificial neural networks model[J]. The Chinese Journal of Geological Hazard and Control,2022,33(2):133 − 140. (in Chinese with English abstract)]
CHEN Shuiman, ZHAO Huilong, XU Zhen, et al. Landslide risk assessment in Nanping City based on artificial neural networks model［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（2）: 133 − 140. （in Chinese with English abstract）
[12] 屠水云,张钟远,付弘流,等. 基于CF与CF-LR模型的地质灾害易发性评价[J]. 中国地质灾害与防治学报,2022,33(2):96 − 104. [TU Shuiyun,ZHANG Zhongyuan,FU Hongliu,et al. Geological hazard susceptibility evaluation based on CF and CF-LR model[J]. The Chinese Journal of Geological Hazard and Control,2022,33(2):96 − 104. (in Chinese with English abstract)]
TU Shuiyun, ZHANG Zhongyuan, FU Hongliu, et al. Geological hazard susceptibility evaluation based on CF and CF-LR model［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（2）: 96 − 104. （in Chinese with English abstract）
[13] 曾斌,吕权儒,寇磊,等. 基于Logistic回归和随机森林的清江流域长阳库岸段堆积层滑坡易发性评价[J]. 中国地质灾害与防治学报,2023,34(4):105 − 113. [ZENG Bin,LYU Quanru,KOU Lei,et al. Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4):105 − 113. (in Chinese with English abstract)]
ZENG Bin, LYU Quanru, KOU Lei, et al. Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（4）: 105 − 113. （in Chinese with English abstract）
[14] 冉星星. 基于机器学习的深圳市地面坍塌灾害风险评估[D]. 荆州:长江大学,2023. [RAN Xingxing. Risk assessment of ground collapse in Shenzhen based on machine learning[D]. Jingzhou:Yangtze University,2023. (in Chinese with English abstract)]
RAN Xingxing. Risk assessment of ground collapse in Shenzhen based on machine learning［D］. Jingzhou: Yangtze University, 2023. （in Chinese with English abstract）
[15] 王子童. 煤矿采空区地面塌陷危险性评价与三维可视化[D]. 西安:西安科技大学,2021. [WANG Zitong. Risk evaluation and three-dimensional visualization of ground collapse in coal mine goaf[D]. Xi’an:Xi’an University of Science and Technology,2021. (in Chinese with English abstract)]
WANG Zitong. Risk evaluation and three-dimensional visualization of ground collapse in coal mine goaf［D］. Xi’an: Xi’an University of Science and Technology, 2021. （in Chinese with English abstract）
[16] NAKAMURA H,KUBOTA T,FURUKAWA M,et al. Unified construction of running track tunnel and crossover tunnel for subway by rectangular shape double track cross-section shield machine[J]. Tunnelling and Underground Space Technology,2003,18(2/3):253 − 262.
[17] 黄发明,胡松雁,闫学涯,等. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J]. 地质科技通报,2022,41(2):79 − 90. [HUANG Faming,HU Songyan,YAN Xueya,et al. Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models[J]. Bulletin of Geological Science and Technology,2022,41(2):79 − 90. (in Chinese with English abstract)]
HUANG Faming, HU Songyan, YAN Xueya, et al. Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models［J］. Bulletin of Geological Science and Technology, 2022, 41（2）: 79 − 90. （in Chinese with English abstract）
[18] GONÇALVES L,SUBTIL A,OLIVEIRA M R,et al. ROC curve estimation:An overview[J]. REVSTAT-Statistical journal,2014,12(1):1 − 20.
[19] 吴宏阳,周超,梁鑫,等. 基于XGBoost模型的三峡库区燕山乡滑坡易发性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5):141 − 152. [WU Hongyang,ZHOU Chao,LIANG Xin,et al. Assessment of landslide susceptibility mapping based on XGBoost model:A case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):141 − 152. (in Chinese with English abstract)]
WU Hongyang, ZHOU Chao, LIANG Xin, et al. Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（5）: 141 − 152. （in Chinese with English abstract）
[20] 牟家琦,庄建琦,王世宝,等. 基于深度神经网络模型的雅安市滑坡易发性评价[J]. 中国地质灾害与防治学报,2023,34(3):157 − 168. [MOU Jiaqi,ZHUANG Jianqi,WANG Shibao,et al. Evaluation of landslide susceptibility in Ya’an City based on depth neural network model[J]. The Chinese Journal of Geological Hazard and Control,2023,34(3):157 − 168. (in Chinese with English abstract)]
MOU Jiaqi, ZHUANG Jianqi, WANG Shibao, et al. Evaluation of landslide susceptibility in Ya’an City based on depth neural network model［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（3）: 157 − 168. （in Chinese with English abstract）
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