广域滑坡易发性多分支网络评估及动态变化分析

doi:10.11947/j.AGCS.2025.20240014

测绘学报 ›› 2025, Vol. 54 ›› Issue (1): 104-122.doi: 10.11947/j.AGCS.2025.20240014

• 摄影测量学与遥感 • 上一篇

广域滑坡易发性多分支网络评估及动态变化分析

吕继超(), 张瑞(), 何旭, 洪瑞凯, 沙马阿各, 刘国祥

西南交通大学地球科学与工程学院，四川　成都　611756

收稿日期:2024-01-09 修回日期:2024-12-10 发布日期:2025-02-17
通讯作者: 张瑞 E-mail:lvjichao@my.swjtu.edu.cn;zhangrui@swjtu.edu.cn
作者简介:吕继超（1996—），男，博士生，研究方向为滑坡智能监测与风险评估。　E-mail：lvjichao@my.swjtu.edu.cn
基金资助:
国家自然科学基金联合基金(U22A20565);国家自然科学基金(42371460);国家重点研发计划(2023YFB2604001);四川省重大科技专项项目(2023ZDZX0030);西藏自治区重点研发计划(XZ202401ZY0057);四川省地质调查研究院科研项目(SCIGS-CZDZX-2024004)

Multi-branch network assessment and dynamic change analysis of wide-area landslide susceptibility

Jichao LÜ(), Rui ZHANG(), Xu HE, Ruikai HONG, Age SHAMA, Guoxiang LIU

Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China

Received:2024-01-09 Revised:2024-12-10 Published:2025-02-17
Contact: Rui ZHANG E-mail:lvjichao@my.swjtu.edu.cn;zhangrui@swjtu.edu.cn
About author:LÜ Jichao (1996—), male, PhD candidate, majors in intelligent monitoring and risk assessment of landslides.　E-mail: lvjichao@my.swjtu.edu.cn
Supported by:
The Joint Funds of the National Natural Science Foundation of China(U22A20565);The National Natural Science Foundation of China(42371460);The National Key Research and Development Program of China(2023YFB2604001);Sichuan Science and Technology Program(2023ZDZX0030);Tibet Autonomous Region Key Research and Development Program(XZ202401ZY0057);The Project of Sichuan Geological Survey and Research Institute(SCIGS-CZDZX-2024004)

摘要/Abstract

摘要：

针对卷积神经网络（CNN）在滑坡易发性评估中因数据通道叠加导致过度关注特定因子的问题，本文提出了一种多分支数据融合的滑坡易发性评估模型，该模型通过多分支结构和自适应定权机制实现多源遥感数据的特征融合，进而借助深度CNN充分提取评价因子的语义信息以准确评估滑坡易发性。试验选取青藏高原东南部作为典型研究区，与随机森林、浅层CNN和ResNet101模型的对比分析表明，本文提出的多分支网络模型在广域滑坡易发性评估方面更具优势，其准确率、精确率、召回率、F₁值、曲线下面积（AUC）和频率比精度均优于现有模型（分别达到0.88、0.89、0.92、0.90、0.92和0.97）。在此基础上，结合连续5年的滑坡易发性评估结果，进一步探讨植被、降雨量等环境因子波动与滑坡易发性指数变化间的内在关联，并通过变异系数掲示滑坡易发性指数时空变化规律。研究结果表明，近5年岷江-大渡河流域、雅砻江流域以及雅鲁藏布江流域受归一化植被指数和局部降雨变化的影响，整体风险均呈现出先增大后减小的变化特征；而金沙江流域和怒江-澜沧江流域植被和降雨量波动较小，滑坡易发性等级总体保持在中高风险水平。本文所提出的模型与方法可为同类区域滑坡风险评估提供参考和借鉴。

关键词: 滑坡易发性评估, 多分支数据融合, 卷积神经网络, 多源遥感, 动态变化

Abstract:

In response to the issue where convolutional neural networks (CNN) in landslide susceptibility assessments overly focus on specific factors due to data channel stacking, this study proposes a multi-branch data fusion model for landslide susceptibility assessment. The model integrates multi-source remote sensing data features through a multi-branch structure and an adaptive weighting mechanism, and then it leverages deep CNNs to fully extract the semantic information of evaluation factors for accurate landslide susceptibility assessment. The experiment selected the southeastern Qinghai-Tibet Plateau as a typical study area, and comparative analyses with the random forest, shallow CNN, and ResNet101 models showed that the proposed multi-branch network model offers superior performance in wide-area landslide susceptibility assessment. The model outperforms existing models in accuracy, precision, recall, F₁ score, area under the curve (AUC), and frequency ratio accuracy, with respective values of 0.88, 0.89, 0.92, 0.90, 0.92, and 0.97. Based on these results, this study further investigates the intrinsic relationship between fluctuations in environmental factors, such as vegetation and rainfall, and changes in the landslide susceptibility index by combining the landslide susceptibility assessment results from five consecutive years. The study also explored temporal and spatial variations in the landslide susceptibility index using the coefficient of variation. The results indicate that in the past five years, the Minjiang-Daduhe River Basin, Yalong River Basin, and Yarlung Tsangpo River Basin have experienced a trend of first increasing and then decreasing landslide risk, influenced by the normalized difference vegetation index (NDVI) and local rainfall variations. In contrast, the Jinsha River Basin and Nujiang-Lancang River Basin have shown smaller fluctuations in vegetation and rainfall, with landslide susceptibility levels generally remaining at medium to high-risk levels. The proposed model in this study can be used as references for landslide risk assessments in similar regions.

Key words: landslide susceptibility assessments, multi-branch data fusion, convolutional neural networks, multi-source remote sensing, dynamic changes

中图分类号:

P237

吕继超, 张瑞, 何旭, 洪瑞凯, 沙马阿各, 刘国祥. 广域滑坡易发性多分支网络评估及动态变化分析[J]. 测绘学报, 2025, 54(1): 104-122.

Jichao LÜ, Rui ZHANG, Xu HE, Ruikai HONG, Age SHAMA, Guoxiang LIU. Multi-branch network assessment and dynamic change analysis of wide-area landslide susceptibility[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 104-122.

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数据	来源	数据	来源
DEM	https://gdex.cr.usgs.	地表粗糙度	30 m SRTM DEM
地层岩性	http://dcc.ngac.org.cn	地形起伏度	30 m SRTM DEM
断层	https://geocloud.cgs	地形湿度指数	30 m SRTM DEM
地面峰值加速度	http://gmo.gfz-potsdam.de/	曲率	30 m SRTM DEM
河流	https://www.openstreetmap.org/	平面曲率	30 m SRTM DEM
道路	https://www.openstreetmap.org/	剖面曲率	30 m SRTM DEM
降雨	https://gpm.nasa.gov	坡向	30 m SRTM DEM
NDVI	GEE/Landsat-8	坡度	30 m SRTM DEM

模型	准确度	精确度	召回率	F₁值
随机森林	0.77	0.78	0.78	0.78
浅层CNN	0.70	0.66	0.87	0.75
ResNet101	0.74	0.72	0.77	0.74
多分支网络模型	0.88	0.89	0.92	0.90

模型	易发性等级	分级栅格数	百分比/（%）	滑坡点数	百分比/（%）	频率比
	极低易发区	103 224 760	33.93	29	1.70	0.05
	低易发区	89 398 692	29.38	135	7.93	0.27
随机森林	中等易发区	56 627 969	18.61	276	16.21	0.87
	高易发区	41 894 540	13.77	650	38.19	2.77
	极高易发区	13 109 914	4.31	612	35.96	8.34
	极低易发区	23 860 212	7.84	14	0.82	0.10
	低易发区	150 562 987	49.48	320	18.80	0.38
浅层CNN	中等易发区	58 941 341	19.37	312	18.33	0.95
	高易发区	66 053 031	21.70	915	53.76	2.48
	极高易发区	4 838 304	4.30	141	8.28	5.21
	极低易发区	77 173 862	25.36	112	6.58	0.26
	低易发区	69 152 928	22.73	158	9.28	0.41
ResNet101	中等易发区	65 258 032	21.45	347	20.39	0.96
	高易发区	59 334 304	19.50	598	35.14	1.80
	极高易发区	33 336 749	10.96	487	28.61	2.61
	极低易发区	151 964 854	49.94	26	1.52	0.03
	低易发区	47 612 221	15.64	42	2.46	0.16
多分支网络模型	中等易发区	58 740 480	19.30	126	7.40	0.38
	高易发区	33 216 672	10.91	386	22.67	2.08
	极高易发区	12 721 648	4.18	1122	65.92	15.77

模型	准确度	精确度	召回率	F₁值
去除自适应定权机制	0.84	0.81	0.90	0.85
去除伪孪生网络结构	0.82	0.79	0.88	0.83

模型	易发性等级	分级栅格数	百分比/（%）	滑坡点数	百分比/（%）	频率比
去除自适应定权机制	极低易发区	78 971 539	25.96	29	1.70	0.07
	低易发区	61 998 992	20.38	70	4.11	0.20
	中等易发区	50 522 720	16.61	143	8.40	0.51
	高易发区	46 883 968	15.41	372	21.86	1.42
	极高易发区	65 878 656	21.65	1088	63.92	2.95
去除伪孪生网络结构	极低易发区	89 695 094	29.48	135	7.93	0.27
	低易发区	70 354 349	23.12	200	11.75	0.51
	中等易发区	62 070 096	20.40	318	18.68	0.92
	高易发区	43 871 088	14.42	419	24.62	1.71
	极高易发区	38 265 248	12.58	630	37.02	2.94

广域滑坡易发性多分支网络评估及动态变化分析

Multi-branch network assessment and dynamic change analysis of wide-area landslide susceptibility

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