基于机器学习的滑坡易发性预测建模及其主控因子识别

黄发明; 胡松雁; 闫学涯; 李明; 王俊宇; 李文彬; 郭子正; 范文彦

doi:10.19509/j.cnki.dzkq.2021.0087

基于机器学习的滑坡易发性预测建模及其主控因子识别

doi: 10.19509/j.cnki.dzkq.2021.0087

基金项目:

国家自然科学基金项目 41807285

国家重点研发计划项目 2019YFC0605001

中国博士后基金项目 2019M652287

中国博士后基金项目 2020T130274

江西省博士后基金项目 2019KY08

详细信息

作者简介:
黄发明(1988—)，男，副教授，主要从事滑坡易发性预测研究工作。E-mail: faminghuang@ncu.edu.cn

通讯作者:
李文彬(1986—)，女，讲师，主要从事滑坡易发性建模研究工作。E-mail: 351113619004@email.ncu.edu.cn

中图分类号: P642.22
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- 文章访问数: 2610
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-08

Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models

摘要

摘要: 不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异, 另外如何有效识别滑坡易发性的主控因子意义重大。针对上述问题, 以支持向量机(support vector machine, 简称SVM)和随机森林(random forest, 简称RF)为例探讨了基于机器学习的滑坡易发性预测及其不确定性, 创新地提出了"权重均值法"来综合计算出更准确的滑坡主控因子。首先获取陕西省延长县滑坡编录和10类基础环境因子, 将因子频率比值作为SVM和RF的输入变量; 再将滑坡与随机选择的非滑坡样本划分为训练集和测试集, 用训练好的机器学习预测出滑坡易发性并制图; 最后用受试者工作曲线、均值和标准差等来评估建模不确定性, 并计算滑坡主控因子。结果表明: ①机器学习能有效预测出区域滑坡易发性, RF预测的滑坡易发性精度高于SVM, 而其不确定性低于SVM, 但两者的易发性分布规律整体相似; ②权重均值法计算出延长县滑坡主控因子依次是坡度、高程和岩性。实例分析和文献综述显示RF模型相较于其他机器学习模型属于可靠性较高的易发性模型。
- 滑坡易发性预测 /
- 不确定性分析 /
- 主控因子识别 /
- 支持向量机 /
- 随机森林
Abstract: The modelling processes and uncertainties of various machine learning models for landslide susceptibility prediction (LSP) are different, and effectively identifying the main conditioning factors of landslide susceptibility is of great significance. Aiming at these problems, this study aims to discuss the LSP processes and the uncertainties of landslide susceptibility based on machine learning models, namely, support vector machine (SVM) and random forest (RF), and then to innovatively propose the "weighted mean method" for calculating more accurate landslide main control factors. First, the landslide inventories and 10 basic environmental factors of Yanchang County in Shaanxi Province are obtained, and the frequency ratios (FRs) of the environmental factors are taken as the input variables of the SVM and RF models.Then, the landslide and randomly selected nonlandslide samples are divided into model training and testing datasets. Furthermore, the trained RF and SVM models are used to predict the landslide susceptibility and draw the landslide susceptibility prediction (LSP) map.Finally, the uncertainties of LSP modelling are evaluated by the receiver operating characteristic (ROC) curve, mean value and standard deviation, and the main landslide control factors are calculated.The results show that ① Machine learning models can effectively predict the susceptibility of regional landslides. The accuracy of RF in LSP is higher, and its uncertainties are lower than those of SVM. As a whole, the landslide susceptibility distribution rules of the two models are similar.②The main control factors of landslide susceptibility in Yanchang County calculated by the weighted mean method are slope, elevation and lithology.③Case studies and literature reviews show that the RF model is a more reliable susceptibility model than other types of machine learning models.
- landslide susceptibility prediction /
- uncertainties analysis /
- identification of the main control factors /
- support vector machine /
- random forest

HTML全文

图 1 机器学习模型预测滑坡易发性的流程图

Figure 1. Flowchart of the machine learning model for predicting landslide susceptibility

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图 2 研究区概况及滑坡编录

Figure 2. Overview of the study area and landslide catalog

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图 3 延长县滑坡基础环境因子(剖面曲率省略)

Figure 3. Basic environmental factors of landslide in Yanchang County

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图 4 滑坡易发性分级图

Figure 4. Classification diagram of landslide susceptibility

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图 5 RF和SVM模型预测滑坡易发性的ROC曲线

Figure 5. ROC curves of the RF and SVM models predicting landslide susceptibility

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图 6 RF和SVM模型预测滑坡易发性指数分布规律

Figure 6. Distribution rule of the RF and SVM models predicting landslide susceptibility indexes

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图 7 滑坡环境因子重要性排名

Figure 7. Importance ranking of landslide environmental factor

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图 8 滑坡环境因子平均重要性排名

Figure 8. Means importance ranking of landslide environmental factor

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表 1 各基础环境因子的属性区间分级及其频率比值

Table 1. Attribute interval and frequency ratio of each evaluation factor

基础环境因子	变量值	全区栅格/个	栅格比例/%	滑坡栅格/个	滑坡栅格比例/%	频率比
坡向/(°) (连续型)	-1	190	0.007	0	0.000	0.000
	[0, 22.5)	222 188	8.472	211	6.200	0.732
	[22.5, 67.5)	304 138	11.597	576	16.926	1.459
	[67.5, 112.5)	484 681	18.482	481	14.135	0.765
	[112.5, 157.5)	345 859	13.188	636	18.689	1.417
	[157.5, 202.5)	254 226	9.694	430	12.636	1.303
	[202.5, 247.5)	333 472	12.716	451	13.253	1.042
	[247.5, 292.5)	382 265	14.576	241	7.082	0.486
	[292.5, 337.5)	295 463	11.267	377	11.078	0.983
	[337.5, 360]	222 188	8.472	211	6.200	0.732
坡度/(°) (连续型)	[0, 6.10)	262 663	10.016	38	1.117	0.111
	[6.10, 10.45)	420 175	16.022	148	4.349	0.271
	[10.45, 14.20)	485 296	18.505	369	10.843	0.586
	[14.20, 17.55)	477 773	18.218	638	18.748	1.029
	[17.55, 20.70)	417 670	15.927	803	23.597	1.482
	[20.70, 23.86)	309 745	11.811	741	21.775	1.844
	[23.86, 27.61)	186 825	7.124	540	15.868	2.227
	[27.61, 50.29]	62 335	2.377	126	3.703	1.558
平面曲率(连续型)	[0, 9.91)	531 824	20.279	1028	30.209	1.490
	[9.91, 18.21)	536 386	20.453	938	27.564	1.348
	[18.21, 27.48)	415 938	15.860	563	16.544	1.043
	[27.48, 37.39)	298 275	11.374	338	9.932	0.873
	[37.39, 47.93)	229 817	8.763	194	5.701	0.651
	[47.93, 59.12)	188 588	7.191	154	4.525	0.629
	[59.12, 70.62)	160 843	6.133	78	2.292	0.374
	[70.62, 81.49]	260 811	9.945	110	3.232	0.325
剖面曲率(连续型)	[0, 2.46)	518 211	19.760	691	20.306	1.028
	[2.46, 4.34)	614 610	23.436	839	24.655	1.052
	[4.34, 6.33)	531 797	20.278	736	21.628	1.067
	[6.33, 8.33)	377 404	14.391	492	14.458	1.005
	[8.33, 10.44)	264 686	10.093	294	8.639	0.856
	[10.44, 12.90)	181 130	6.907	223	6.553	0.949
	[12.90, 15.95)	99 956	3.812	94	2.762	0.725
	[15.95, 29.90]	34 688	1.323	34	0.999	0.755
高程/m (连续型)	[473.14, 656.00)	65 566	2.500	0	0.000	0.000
	[656.00, 772.04)	162 922	6.213	0	0.000	0.000
	[772.04, 866.99)	282 304	10.765	336	9.874	0.917
	[866.99, 944.35)	422 849	16.124	1 078	31.678	1.965
	[944.35, 1 014.68)	552 133	21.054	1 068	31.384	1.491
	[1 014.68, 1 085.01)	551 281	21.021	605	17.778	0.846
	[1 085.01, 1 165.89)	395 921	15.097	207	6.083	0.403
	[1 165.89, 1 369.84]	189 506	7.226	109	3.203	0.443
NDVI (连续型)	[0.054, 0.161)	4 323	0.165	0	0.000	0.000
	[0.161, 0.222)	222 528	8.485	346	10.167	1.198
	[0.222, 0.248)	347 566	13.253	503	14.781	1.115
	[0.248, 0.271)	522 175	19.911	590	17.338	0.871
	[0.271, 0.290)	651 814	24.855	837	24.596	0.990
	[0.290, 0.316)	726 302	27.695	958	28.152	1.016
	[0.316, 0.514)	147 748	5.634	169	4.966	0.881
	[0.514, 0.880]	26	0.001	0	0.000	0.000
NDBI (连续型)	[0.015, 0.032)	166	0.006	0	0.000	0.000
	[0.032, 0.523)	20 500	0.782	3	0.088	0.113
	[0.523, 0.550)	144 671	5.517	263	7.728	1.401
	[0.550, 0.569)	328 825	12.539	477	14.017	1.118
	[0.569, 0.585)	461 789	17.609	517	15.192	0.863
	[0.585, 0.601)	638 451	24.345	610	17.925	0.736
	[0.601, 0.617)	636 591	24.274	816	23.979	0.988
	[0.617, 0.701]	391 489	14.928	717	21.070	1.411
MNDWI (连续型)	[0.192, 0.328)	414 411	15.802	749	22.010	1.393
	[0.328, 0.356)	732 165	27.919	866	25.448	0.912
	[0.356, 0.384)	565 063	21.547	559	16.427	0.762
	[0.384, 0.418)	421 031	16.055	479	14.076	0.877
	[0.418, 0.455)	292 549	11.155	421	12.371	1.109
	[0.455, 0.513)	192 388	7.336	329	9.668	1.318
	[0.513, 0.640)	4 737	0.181	0	0.000	0.000
	[0.640, 0.981]	138	0.005	0	0.000	0.000
总辐射(连续型)	[0, 90)	6 815	0.260	0	0.000	0.000
	[90, 170)	68 498	2.612	98	2.880	1.103
	[170, 185)	187 607	7.154	456	13.400	1.873
	[185, 198)	276 782	10.554	448	13.165	1.247
	[198, 211)	339 667	12.952	507	14.899	1.150
	[211, 225)	424 230	16.177	589	17.308	1.070
	[225, 239)	537 676	20.503	567	16.662	0.813
	[239, 255]	781 207	29.789	738	21.687	0.728
岩性(离散型)	泥岩和油页岩	179 598	6.848	0	0.000	0.000
	单独泥岩	140 332	5.351	364	10.696	1.999
	砂岩与泥岩	134 435	5.126	425	12.489	2.436
	石英砂岩	2 231	0.085	0	0.000	0.000
	风积和洪积黄土	2 165 886	82.589	2 614	76.815	0.930

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表 2 RF和SVM模型易发性图的频率比精度分析

Table 2. Frequency ratio precision analysis of susceptibility graphs of RF and SVM models

模型	易发性等级	全区栅格/个	全区栅格比例/%	滑坡栅格/个	滑坡栅格比例/%	频率比(FR)
RF	极低	695 561	26.52	9	0.26	0.010
	低	641 183	24.45	39	1.15	0.047
	中	575 729	21.95	153	4.50	0.205
	高	428 719	16.35	478	14.05	0.859
	极高	281 290	10.73	2 724	80.05	7.463
SVM	极低	773 173	29.48	84	2.47	0.084
	低	699 525	26.67	346	10.17	0.381
	中	388 165	14.80	357	10.49	0.709
	高	337 687	12.88	568	16.69	1.296
	极高	423 932	16.17	2 048	60.18	3.723

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