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现代地质 ›› 2018, Vol. 32 ›› Issue (03): 611-622.DOI: 10.19657/j.geoscience.1000-8527.2018.03.19

• 水文地质与环境地质学 • 上一篇    下一篇

基于逻辑回归模型的泥石流易发性评价与检验: 以金沙江上游奔子栏—昌波河段为例

吴赛儿1(), 陈剑1(), ZHOU Wendy2, 高玉欣1, 徐能雄1   

  1. 1.中国地质大学(北京) 工程技术学院,北京 100083
    2. Colorado School of Mines, Department of Geology and Geological Engineering, Colorado Denver 80401
  • 收稿日期:2017-06-13 修回日期:2018-05-10 出版日期:2018-06-10 发布日期:2023-09-22
  • 通讯作者: 陈剑
  • 作者简介:陈剑,男,副教授,1975年出生,地质工程专业,主要从事工程地质、地质灾害与环境研究。Email:jianchen@cugb.edu.cn
    吴赛儿,女,硕士研究生,1992年出生,地质工程专业,主要从事工程地质与地质灾害研究。Email: cielo@cugb.edu.cn
  • 基金资助:
    国家自然科学基金项目(41571012);国家自然科学基金项目(41230743);中央高校基本科研业务费专项资金资助项目(2652015060)

Debris-flow Susceptibility Assessment and Validation Based on Logistic Regression Model: An Example from the Benzilan-Changbo Segment of the Upper Jinshajiang River

WU Saier1(), CHEN Jian1(), WENDY Zhou2, GAO Yuxin1, XU Nengxiong1   

  1. 1. School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
    2. Department of Geology and Geological Engineering, Colorado School of Mines, Colorado Denver 80401, USA
  • Received:2017-06-13 Revised:2018-05-10 Online:2018-06-10 Published:2023-09-22
  • Contact: CHEN Jian

摘要:

基于地理信息系统(ArcGIS10.0)平台和小流域单元,采用逻辑回归(LR)模型对金沙江上游(奔子栏—昌波河段)干热河谷区进行泥石流易发性评价,并对预测结果进行总体检验与随机个案检验。评价与检验结果表明,得到的最优指标组合下LR评价模型的AUC值为82.7%;预测的极高易发区、高易发区面积合占全区面积的35.98%,实发泥石流面积占泥石流总面积的65.03%;在个案检验中,位于各等级分区的检验组样本实发泥石流比例随着分区易发性等级降低,依次为91.7%(极高)、75.0%(高)、36.4%(中等)、16.7%(低)、0(极低),表明评价效果良好。研究区泥石流集中发育于金沙江沿岸的东北部、中部和西南部,主导性的评价指标依次为距主干道路距离、岩性、距断裂带距离、雨季月平均降雨量。人类活动与季节性降雨为研究区干热河谷泥石流的主要诱发条件。基于逻辑回归模型的泥石流易发性评价方法提高了泥石流发生可能性的预测精度,可为干热河谷区泥石流预测预警和防治提供参考依据。

关键词: 干热河谷区, 泥石流, 逻辑回归模型, 易发性评价, 金沙江

Abstract:

In this paper, we apply logistic regression (LR) model (using ArcGIS10.0) and catchment units to predict the distributions of debris-flow susceptibility, taking the Benzilan-Changbo segment of the upper reach of the Jinshajiang River as an example. Overall and random case testing was conducted to validate the results. The AUC values of the LR evaluation model were based on the optimum index combination of 82.7%.The prediction results show the extremely high debris-flow susceptibility areas cover about 35.98% of the total study area, whereas the actual debris-flow area accounts for 65.03% of the total observed debris-flow area. In the case test, the ratios of actual debris-flow samples of testing dataset (in each grade partition with decreasing susceptibility levels) are 91.7%(extremely high), 75.0%(high), 36.4%(moderate), 16.7%(low) and 0(extremely low), representing favorable prediction results of the debris-flow susceptibility map. High susceptibility areas are mainly distributed in the northeastern, middle and southwestern parts of the Jinshajiang river bank. The major indexes include the distance to major highways, lithology, distance to fault zone, and average monthly precipitation in the rainy season, which indicate that human activities and seasonal rainstorm are the main triggers of debris flow in the semiarid mountainous Jinshajiang valley. We suggest that debris-flow susceptibility assessment based on LR modeling can improve the prediction accuracy of potential debris flows, and provides an important reference for forecasting, warning and preventing and mitigating debris-flow risk in semiarid mountainous valleys.

Key words: semiarid mountainous valley, debris flow, logistic regression model, susceptibility assessment, Jinshajiang River

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