基于随机森林算法的天津市滨海地区地面沉降模拟

耿芳; 白苏娜; 齐文艳; 于金山; 毛华; 张梅; 席雪萍; 高学飞; 罗福贵

doi:10.19509/j.cnki.dzkq.tb20240119

基于随机森林算法的天津市滨海地区地面沉降模拟

doi: 10.19509/j.cnki.dzkq.tb20240119

耿芳^1,,
白苏娜^{1, 2},
齐文艳^{1, 3},
于金山^{1, 3},
毛华¹,
张梅^{1, 2},
席雪萍^4, ,,
高学飞⁴,
罗福贵⁴

1.
国网天津市电力公司, 天津 300010
2.
国网天津市电力公司经济技术研究院, 天津 300171
3.
国网天津市电力公司电力科学与技术研究院, 天津 300384
4.
天津市地质工程勘测设计院有限公司, 天津 300191

基金项目:

国网天津市电力公司科技项目“基于滑坡-沉降地质灾害特性的输变电工程防灾技术研究” 电科-研发2023-48

详细信息

作者简介:
耿芳, E-mail: 15822947395@163.com

通讯作者:
席雪萍, E-mail: xixueping1981@163.com

中图分类号: P642.26
计量
- 文章访问数: 89
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-26
- 录用日期: 2024-05-21
- 修回日期: 2024-05-20

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

GENG Fang^1
,,
BAI Suna^{1, 2},
QI Wenyan^{1, 3},
YU Jinshan^{1, 3},
MAO Hua¹,
ZHANG Mei^{1, 2},
XI Xueping^{4
, ,},
GAO Xuefei⁴,
LUO Fugui⁴

1.
State Grid Tianjin Electric Power Company, Tianjin 300010, China
2.
Economic and Technical Research Institute of State Grid Tianjin Electric Power Company, Tianjin 300171, China
3.
Tianjin Electric Power Science and Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China
4.
Tianjin Geological Engineering Survey and Design Institute Co., Ltd., Tianjin 300191, China

More Information

Author Bio:
GENG Fang, E-mail: 15822947395@163.com

Corresponding author: XI Xueping, E-mail: xixueping1981@163.com

摘要

摘要:
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(R²=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。
- 地面沉降 /
- 滨海地区 /
- 随机森林 /
- 机器学习 /
- 天津市
Abstract: 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 (R²=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: ① utilization of spatial distribution data for training ground subsidence models; ② identification of significant controlling factors based on physical mechanisms; ③ assessment 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.
- ground subsidence /
- coastal area /
- random forest /
- machine learning /
- Tianjin
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 研究区范围及其水准点空间分布

Figure 1. Scope of the study area and its spatial distribution of benchmark points

下载: 全尺寸图片幻灯片

图 2 天津滨海地区地面沉降量等值线图

Figure 2. Contour map of ground subsidence in Tianjin coastal area

下载: 全尺寸图片幻灯片

图 3 滨海地区砂层(a)和黏土层(b)厚度比值分布图

砂层厚度比是指砂层厚度/沉积物总厚度；黏土层厚度比是指黏性土厚度/沉积物总厚度

Figure 3. Distribution map of thickness ratio of sand(a) and clay(b) in coastal areas

下载: 全尺寸图片幻灯片

图 4 天津市滨海地区含水组水位差(a)、水平渗透系数(b)以及给水度与储水率(c)分布图

水位差是指含水组研究时间段内水位差异，m；水平渗透系数是指含水层水平方向渗透能力，m/d；给水度, 储水率是指地下水位下降一个单位时，单位面积岩石柱体在重力作用下所释放出的水的体积

Figure 4. Distribution map of water level changes(a), horizontal hydraulic conductivity(b) and specific yield or specific storage(c) of aquifers in Tianjin coastal area

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图 5 基于随机森林的地面沉降预测模型示意图

Figure 5. Schematic diagram of ground subsidence prediction model based on random forest

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图 6 天津市滨海地区地面沉降随机森林(a)、多元线性回归(b)预测值与观测值对比验证

Figure 6. Comparison of predicted and observed values of ground subsidence using random forest(a) and multilinear regression(b) in Tianjin coastal area

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图 7 天津滨海地区地面沉降量影响因素重要性图

Figure 7. Importance of factors influencing ground subsidence in Tianjin coastal area

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表 1 天津市滨海地区含水组Pearson参数相关性分析

Table 1. Pearson correlation coefficient analysis of aquifer lithology in Tianjin coastal area

		含水组水位差					含水组水平渗透系数					含水组给水度或储水率					岩性(厚度比)		沉降量
		1	2	3	4	5	1	2	3	4	5	1	2	3	4	5	砂层	黏性	沉降量
含水组水位差	1	1.00	0.50	-0.72	0.39	0.97		-0.67		0.53		0.29	0.00	0.00	0.42	0.00	-0.49	-0.55	-0.04
	2	0.50	1.00	-0.32	0.07	0.62		-0.53		0.64		0.06	0.00	0.00	0.28	0.00	-0.33	-0.49	-0.21
	3	-0.72	-0.32	1.00	-0.75	-0.63		0.40		-0.20		-0.31	0.00	0.00	-0.05	0.00	0.06	0.07	0.05
	4	0.39	0.07	-0.75	1.00	0.31		0.00		-0.15		0.18	0.00	0.00	-0.09	0.00	0.36	0.26	-0.21
	5	0.97	0.62	-0.63	0.31	1.00		-0.72		0.66		0.16	0.00	0.00	0.48	0.00	-0.55	-0.64	-0.05
含水组水平渗透系数	1
	2	-0.67	-0.53	0.40	0.00	-0.72		1.00		-0.74		0.07	0.00	0.00	-0.22	0.00	0.81	0.59	-0.09
	3
	4	0.53	0.64	-0.20	-0.15	0.66		-0.74		1.00		-0.20	0.00	0.00	0.30	0.00	-0.67	-0.82	0.06
	5
含水组给水度或储水率	1	0.29	0.06	-0.31	0.18	0.16		0.07		-0.20		1.00	0.00	0.00	0.02	0.00	0.11	0.10	-0.15
	2	0.00	0.00	0.00	0.00	0.00		0.00		0.00		0.00	1.00	-1.00	0.00	1.00	0.00	0.00	0.00
	3	0.00	0.00	0.00	0.00	0.00		0.00		0.00		0.00	-1.00	1.00	0.00	-1.00	0.00	0.00	0.00
	4	0.42	0.28	-0.05	-0.09	0.48		-0.22		0.30		0.02	0.00	0.00	1.00	0.00	-0.20	-0.31	0.01
	5	0.00	0.00	0.00	0.00	0.00		0.00		0.00		0.00	1.00	-1.00	0.00	1.00	0.00	0.00	0.00
岩性(厚度比)	砂岩	-0.49	-0.33	0.06	0.36	-0.55		0.81		-0.67		0.11	0.00	0.00	-0.20	0.00	1.00	0.66	-0.24
岩性(厚度比)	黏土层	-0.55	-0.49	0.07	0.26	-0.64		0.59		-0.82		0.10	0.00	0.00	-0.31	0.00	0.66	1.00	-0.05
沉降量		-0.04	-0.21	0.05	-0.21	-0.05		-0.09		0.06		-0.15	0.00	0.00	0.01	0.00	-0.24	-0.05	1.00
注：岩性(厚度比例)是指砂层厚度或黏性土厚度/沉积物总厚度；“—“是指参数没有相关性，不做讨论

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