基于半监督机器学习的滑坡易发性预测建模

doi:10.3785/j.issn.1008-973X.2021.09.012

浙江大学学报(工学版)

2021, Vol. 55

Issue (9): 1705-1713 DOI: 10.3785/j.issn.1008-973X.2021.09.012

土木工程、水利工程

基于半监督机器学习的滑坡易发性预测建模

黄发明1(

),潘李含1,姚池1,周创兵2,姜清辉2,常志璐1

1. 南昌大学建筑工程学院，江西南昌 330031
2. 武汉大学土木建筑工程学院，湖北武汉 430072

Landslide susceptibility prediction modelling based on semi-supervised machine learning

Fa-ming HUANG1(

),Li-han PAN1,Chi YAO1,Chuang-bing ZHOU2,Qing-hui JIANG2,Zhi-lu CHANG1

1. School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
2. School of Civil Engineering, Wuhan University, Wuhan 430072, China

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摘要：

为了克服滑坡编录样本不足、扩充滑坡样本较困难、主观随机选择的非滑坡样本准确性较低等缺点，以江西省南康区为例，拟用半监督卡方自交互侦测决策树(SSCHAID)和半监督反向传播神经网络(SSBPNN)进行滑坡易发性预测(LSP), 在已知滑坡样本和随机选取的非滑坡样本基础上，用全监督机器学习将初始LSP划分成不同级别；将高分辨率遥感影像和初始滑坡易发性图中的极高易发区叠加，筛选一定数量的潜在滑坡栅格单元扩充滑坡样本；从极低易发区选取非滑坡栅格单元组合成新的输出变量；将新的输出变量导入全监督机器学习，获得最终LSP并评价其精度. 结果表明：半监督机器学习的LSP精度远高于全监督机器学习的LSP精度.

关键词： 滑坡易发性预测 (LSP); 半监督机器学习; 卡方自交互侦测决策树 (CHAID); BP神经网络(BPNN); 地理信息系统(GIS)

Abstract:

A semi-supervised Chi-square self-interactive detection decision tree (SSCHAID) and a semi-supervised back-propagation neural network (SSBPNN) were used for landslide susceptibility prediction (LSP) by taking the Nankang of Jiangxi Province as a case, in order to overcome the shortcomings such as insufficient landslide inventories, difficulty in expanding landslide inventories and subjectively randomly selected non-landslides have low accuracy. Based on the known landslides and randomly selected non-landslides, the initial LSP was divided into different levels by supervised machine learning. The high-resolution remote sensing image was superimposed with the very high susceptibility area in the initial landslide susceptibility map, and a certain number of potential landslide grids were selected to expand landslide inventories. Non-landslide grids were selected from very low susceptibility areas and combined into new output variables. The new output variables were imported into supervised machine learning to obtain the final LSP and evaluate its accuracy. Results show that the accuracy of LSP by semi-supervised machine learning is significantly higher than that of supervised machine learning.

Key words: landslide susceptibility prediction (LSP) semi-supervised machine learning Chi-squared automatic interaction detector (CHAID) BP neural network (BPNN) geographic information system (GIS)

收稿日期: 2020-09-04 出版日期: 2021-10-20

CLC:

P 642.22

基金资助: 国家自然科学基金资助项目(41807285,41762020,51879127,51769014)；江西省自然科学基金资助项目(20192BAB216034,20192ACB2102,20192ACB20020)；中国博士后面上基金资助项目(2019M652287,2020T130274)；江西省博士后基金资助项目(2019KY08);研究生创新专项资金资助项目(YC2020-S120)

作者简介: 黄发明(1988—)，男，副教授，博士，从事滑坡易发性预测研究. orcid. org/0000-0001-9037-9085. E-mail： faminghuang@ncu.edu.cn

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引用本文:

黄发明,潘李含,姚池,周创兵,姜清辉,常志璐. 基于半监督机器学习的滑坡易发性预测建模[J]. 浙江大学学报(工学版), 2021, 55(9): 1705-1713.

Fa-ming HUANG,Li-han PAN,Chi YAO,Chuang-bing ZHOU,Qing-hui JIANG,Zhi-lu CHANG. Landslide susceptibility prediction modelling based on semi-supervised machine learning. Journal of ZheJiang University (Engineering Science), 2021, 55(9): 1705-1713.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.09.012 或 https://www.zjujournals.com/eng/CN/Y2021/V55/I9/1705

图 1 半监督机器学习预测滑坡易发性的建模流程

图 2 遥感解译流程

表 1 滑坡内部控制因素的频率比值

图 3 南康区相关高分辨率航片及部分控制因素

图 4 各模型预测的滑坡易发性分布图

图 5 全监督和半监督机器学习预测滑坡易发性ROC曲线

表 2 4个模型的统计学精度

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