Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake

ZHAO Jiayi; TIAN Shujun; LI Kai; HOU Pengli

doi:10.16031/j.cnki.issn.1003-8035.202306035

2024 Vol. 35, No. 1

Article Contents

Article navigation > The Chinese Journal of Geological Hazard and Control > 2024 > 35(1): 51-59

Next Article Previous Article

ZHAO Jiayi, TIAN Shujun, LI Kai, HOU Pengli. Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(1): 51-59. doi: 10.16031/j.cnki.issn.1003-8035.202306035

Citation:

ZHAO Jiayi, TIAN Shujun, LI Kai, HOU Pengli. Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(1): 51-59. doi: 10.16031/j.cnki.issn.1003-8035.202306035

Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake

1.
School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang, Sichuan　621010, China

More Information

Corresponding author: TIAN Shujun, tsj19800702@163.com

Received Date: 08 June 2023

Revised Date: 09 November 2023

Publish Date: 25 February 2024

Abstract

Abstract

Accurately and scientifically mapping debris flow susceptibility and the determination of key controlling factors and their contribution rates are essential foundations for regional debris flow early warning, forecasting and risk management. The article takes the upper reaches of the Minjiang River as the research area, with small watersheds as evaluation units. Five different machine learning models were employed to construct evaluation models for the susceptibility of debris flows in the upper reaches of the Minjiang River. Quantitative analyses were conducted on the susceptibility of debris flows and the contribution rates of evaluation factors before and after the Wenchuan earthquake. The results indicate that: (1) Integrated machine learning models exhibit higher ACC and AUC values than the shallow machine learning models, with the random forest model performing the best in the assessment of debris flow susceptibility before and after the earthquake; (2) The occurrence rate of debris flow before and after the earthquakes gradually increases with the rise in susceptibility level, and the increment increases with the increase of the level. The occurrence rate of debris flow at all levels is higher after the earthquake than before; (3) The contribution rate of the erosion transmission coefficients before and after the earthquake is significantly higher than that of other factors. This contribution is compounded by the spatial distribution characteristics of the Wenchuan earthquake intensity, further accentuating the spatial distribution pattern of decreasing debris flow development from downstream to upstream in both the main and tributaries following the earthquake.
- debris flow /
- machine learning /
- susceptibility /
- the Wenchuan earthquake /
- upper reaches of the Minjiang River

FullText(HTML)

References

[1]	SIMONI S,ZANOTTI F,BERTOLDI G,et al. Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS[J]. Hydrological Processes,2008,22(4):532 − 545. doi: 10.1002/hyp.6886 CrossRef Google Scholar
[2]	陈怡, 范宣梅. 震后地质灾害易发性评价——以映秀震区为例[J]. 科学技术与工程,2020,20(9):3516 − 3527. [CHEN Yi, FAN Xuanmei. Susceptibility assessment of post-earth-quake geo-hazard in the epicentral area of the 2008 Wenchuan eearthquake near yingxiu town[J]. Science Technology and Engineering,2020,20(9):3516 − 3527. (in Chinese with English abstract)] Google Scholar CHEN Yi, FAN Xuanmei. Susceptibility assessment of post-earth-quake geo-hazard in the epicentral area of the 2008 Wenchuan eearthquake near yingxiu town［J］. Science Technology and Engineering, 2020, 20（9）: 3516 − 3527. （in Chinese with Englishabstract） Google Scholar
[3]	崔鹏, 庄建琦, 陈兴长, 等. 汶川地震区震后泥石流活动特征与防治对策[J]. 四川大学学报(工程科学版),2010,42(5):10 − 19. [CUI Peng, ZHUANG Jianqi, CHEN Xingchang, et al. Characteristics and countermeasures of debris flow in Wenchuan Area after the earthquake[J]. Journal of Sichuan University (Engineering Science Edition),2010,42(5):10 − 19. (in Chinese with English abstract)] Google Scholar CUI Peng, ZHUANG Jianqi, CHEN Xingchang, et al. Characteristics and countermeasures of debris flow in Wenchuan Area after the earthquake［J］. Journal of Sichuan University (Engineering Science Edition), 2010, 42（5）: 10 − 19. （in Chinese with Englishabstract） Google Scholar
[4]	张文涛, 柳金峰, 游勇, 等. 泥石流防治工程损毁度评价——以汶川地区为例[J]. 中国地质灾害与防治学报,2022,33(4):77 − 83. [ZHANG Wentao, LIU Jinfeng, YOU Yong, et al. Damage evaluation of control works against debris flow:A case study in Wenchuan Area[J]. The Chinese Journal of Geological Hazard and Control,2022,33(4):77 − 83. (in Chinese with English abstract)] Google Scholar ZHANG Wentao, LIU Jinfeng, YOU Yong, et al. Damage evaluation of control works against debris flow: A case study in Wenchuan Area［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（4）: 77 − 83. （in Chinese with Englishabstract） Google Scholar
[5]	文强, 胡卸文, 刘波, 等. 四川丹巴梅龙沟“6•17” 泥石流成灾机理分析[J]. 中国地质灾害与防治学报,2022,33(3):23 − 30. [WEN Qiang, HU Xiewen, LIU Bo, et al. Analysis on the mechanism of debris flow in Meilong valley in Danba County on June 17, 2020[J]. The Chinese Journal of Geological Hazard and Control,2022,33(3):23 − 30. (in Chinese with English abstract)] Google Scholar WEN Qiang, HU Xiewen, LIU Bo, et al. Analysis on the mechanism of debris flow in Meilong valley in Danba County on June 17, 2020［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（3）: 23 − 30. （in Chinese with Englishabstract） Google Scholar
[6]	孙滨, 祝传兵, 康晓波, 等. 基于信息量模型的云南东川泥石流易发性评价[J]. 中国地质灾害与防治学报,2022,33(5):119 − 127. [SUN Bin, ZHU Chuanbing, KANG Xiaobo, et al. Susceptibility assessment of debris flows based on information model in Dongchuan, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2022,33(5):119 − 127. (in Chinese with English abstract)] Google Scholar SUN Bin, ZHU Chuanbing, KANG Xiaobo, et al. Susceptibility assessment of debris flows based on information model in Dongchuan, Yunnan Province［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（5）: 119 − 127. （in Chinese with Englishabstract） Google Scholar
[7]	王峰,杨帆,江忠荣,等. 基于沟域单元的康定市泥石流易发性评价[J]. 中国地质灾害与防治学报,2023,34(3):145 − 156. [WANG Feng, YANG Fan, JIANG Zhongrong, et al. Susceptibility assessment of debris flow based on watershed units in Kangding City, Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(3):145 − 156. (in Chinese with English abstract)] Google Scholar WANG Feng, YANG Fan, JIANG Zhongrong, et al. Susceptibility assessment of debris flow based on watershed units in Kangding City, Sichuan Province［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（3）: 145 − 156. （in Chinese with English abstract） Google Scholar
[8]	杨得虎, 朱杰勇, 刘帅, 等. 基于信息量、加权信息量与逻辑回归耦合模型的云南罗平县崩滑灾害易发性评价对比分析[J]. 中国地质灾害与防治学报,2023,34(5):43 − 53. [YANG Dehu, ZHU Jieyong, LIU Shuai, et al. Comparative analyses of susceptibility assessment for landslide disasters based on information value, weighted information value and logistic regression coupled model in Luoping County, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):43 − 53. (in Chinese with English abstract)] Google Scholar YANG Dehu, ZHU Jieyong, LIU Shuai, et al. Comparative analyses of susceptibility assessment for landslide disasters based on information value, weighted information value and logistic regression coupled model in Luoping County, Yunnan Province［J］. The Chinese Journal of Geological Hazard and Control, 2023, 34（5）: 43 − 53. （in Chinese with English abstract） Google Scholar
[9]	易靖松,王峰,程英建,等. 高山峡谷区地质灾害危险性评价——以四川省阿坝县为例[J]. 中国地质灾害与防治学报,2022,33(3):134 − 142. [YI Jingsong, WANG Feng, CHENG Yingjian, et al. Study on the risk assessment of geological disasters in alpine valley area:A case study in Aba County, Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2022,33(3):134 − 142. (in Chinese with English abstract)] Google Scholar YI Jingsong, WANG Feng, CHENG Yingjian, et al. Study on the risk assessment of geological disasters in alpine valley area: A case study in Aba County, Sichuan Province［J］. The Chinese Journal of Geological Hazard and Control, 2022, 33（3）: 134 − 142. （in Chinese with English abstract） Google Scholar
[10]	李巧. 模型融合算法的研究及应用[D]. 武汉:湖北大学,2016. [LI Qiao. Research and Application on Model Blending Algorithm[D]. Wuhan:Hubei University, 2016. (in Chinese with English abstract)] Google Scholar LI Qiao. Research and Application on Model Blending Algorithm［D］. Wuhan: Hubei University, 2016. （in Chinese with English abstract） Google Scholar
[11]	周聂, 侯精明, 陈光照, 等. 基于机器学习的山洪灾害快速预报方法[J]. 水资源保护,2022,38(2):32 − 40. [ZHOU Nie, HOU Jingming, CHEN Guangzhao, et al. A rapid forecasting method for mountain flood disaster based on machine learning algorithms[J]. Water Resources Protection,2022,38(2):32 − 40. (in Chinese with English abstract)] Google Scholar ZHOU Nie, HOU Jingming, CHEN Guangzhao, et al. A rapid forecasting method for mountain flood disaster based on machine learning algorithms［J］. Water Resources Protection, 2022, 38（2）: 32 − 40. （in Chinese with English abstract） Google Scholar
[12]	ABID F. A survey of machine learning algorithms based forest fires prediction and detection systems[J]. Fire Technology,2021,57(2):559 − 590. doi: 10.1007/s10694-020-01056-z CrossRef Google Scholar
[13]	KERN A N,ADDISON P,OOMMEN T,et al. Machine learning based predictive modeling of debris flow probability following wildfire in the intermountain western United States[J]. Mathematical Geosciences,2017,49(6):717 − 735. doi: 10.1007/s11004-017-9681-2 CrossRef Google Scholar
[14]	李奋生, 赵国华, 李勇, 等. 青藏高原东缘的隆升及其水系的响应[J]. 长江流域资源与环境,2016,25(3):420 − 428. [LI Fensheng, ZHAO Guohua, LI Yong, et al. The uplift of the Longmen Shan and the drainage response[J]. Resources and Environment in the Yangtze Basin,2016,25(3):420 − 428. (in Chinese with English abstract)] Google Scholar LI Fensheng, ZHAO Guohua, LI Yong, et al. The uplift of the Longmen Shan and the drainage response［J］. Resources and Environment in the Yangtze Basin, 2016, 25（3）: 420 − 428. （in Chinese with English abstract） Google Scholar
[15]	AZARAFZA M,AZARAFZA M,AKGÜN H,et al. Deep learning-based landslide susceptibility mapping[J]. Scientific Reports,2021,11:24112. doi: 10.1038/s41598-021-03585-1 CrossRef Google Scholar
[16]	贺香勇, 蒋勇, 胡勇. 改进朴素贝叶斯算法在火灾预警中的应用[J]. 中国科学技术大学学报,2022(6):50 − 58+70. [HE Xiangyong, JIANG Yong, HU Yong. Application of a newly developed naive Bayes algorithm in fire alarm[J]. Journal of University of Science and Technology of China,2022(6):50 − 58+70. (in Chinese with English abstract)] Google Scholar HE Xiangyong, JIANG Yong, HU Yong. Application of a newly developed naive Bayes algorithm in fire alarm［J］. Journal of University of Science and Technology of China, 2022（6）: 50 − 58+70. （in Chinese with English abstract） Google Scholar
[17]	CHOI J,OH H J,LEE Hongjin,et al. Combining landslide susceptibility maps obtained from frequency ratio,logistic regression,and artificial neural network models using ASTER images and GIS[J]. Engineering Geology,2012,124:12 − 23. Google Scholar
[18]	MELO R,ZÊZERE J L,ROCHA J,et al. Combining data-driven models to assess susceptibility of shallow slides failure and Run-out[J]. Landslides,2019,16(11):2259 − 2276. Google Scholar
[19]	丁愫, 陈报章, 王瑾, 等. 基于决策树的统计预报模型在臭氧浓度时空分布预测中的应用研究[J]. 环境科学学报,2018,38(8):3229 − 3242. [DING Su, CHEN Baozhang, WANG Jin, et al. An applied research of decision-tree based statistical model in forecasting the spatial-temporal distribution of O₃[J]. Acta Scientiae Circumstantiae,2018,38(8):3229 − 3242. (in Chinese with English abstract)] Google Scholar DING Su, CHEN Baozhang, WANG Jin, et al. An applied research of decision-tree based statistical model in forecasting the spatial-temporal distribution of O₃［J］. Acta Scientiae Circumstantiae, 2018, 38（8）: 3229 − 3242. （in Chinese with English abstract） Google Scholar
[20]	HERNÁNDEZ V A S,MONROY R,MEDINA-PÉREZ M A,et al. A practical tutorial for decision tree induction:evaluation measures for candidate splits and opportunities[J]. ACM Computing Surveys,54(1):18. Google Scholar
[21]	ZHOU Xinzhi,WEN Haijia,ZHANG Yalan,et al. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization[J]. Geoscience Frontiers,2021,12(5):101211. doi: 10.1016/j.gsf.2021.101211 CrossRef Google Scholar
[22]	STATISTICS L B,BREIMAN L. Random Forests[J]. Machine Learning,2001,5 − 32. Google Scholar
[23]	BENTÉJAC C,CSÖRGŐ A,MARTÍNEZ-MUÑOZ G. A comparative analysis of gradient boosting algorithms[J]. Artificial Intelligence Review,2021,54(3):1937 − 1967. doi: 10.1007/s10462-020-09896-5 CrossRef Google Scholar
[24]	CHEN Tianqi,GUESTRIN C. XGBoost:a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 13 - 17,2016,San Francisco,California,USA. ACM,2016:785 − 794. Google Scholar
[25]	张静. 河谷地貌分异下岷江上游泥石流流域演化趋势研究[D]. 绵阳:西南科技大学,2021. [ZHANG Jing. The evolution trend of debris flow basins in the upper Minjiang River under the diversion of river valley geomorphology[D]. Mianyang: Southwest University of Science and Technology, 2021. (in Chinese with English abstract)] Google Scholar ZHANG Jing. The evolution trend of debris flow basins in the upper Minjiang River under the diversion of river valley geomorphology［D］. Mianyang: Southwest University of Science and Technology, 2021. （in Chinese with English abstract） Google Scholar
[26]	高大钊. 《岩土工程勘察规范》(GB 50021—2001)的修订[J]. 建筑结构,2002,32(12):62 − 65. [GAO Dazhao. The revision of code for investigation of geotechnical engineering[J]. Building Structure,2002,32(12):62 − 65. (in Chinese with English abstract)] Google Scholar GAO Dazhao. The revision of code for investigation of geotechnical engineering［J］. Building Structure, 2002, 32（12）: 62 − 65. （in Chinese with English abstract） Google Scholar
[27]	刘纪远. 国家资源环境遥感宏观调查与动态监测研究[J]. 遥感学报,1997,1(3):225 − 230. [LIU Jiyuan. Study on national resources & environment survey and dynamic monitoring using remote sensing[J]. Journal of Remote Sensing,1997,1(3):225 − 230. (in Chinese with English abstract)] Google Scholar LIU Jiyuan. Study on national resources & environment survey and dynamic monitoring using remote sensing［J］. Journal of Remote Sensing, 1997, 1（3）: 225 − 230. （in Chinese with English abstract） Google Scholar
[28]	张晓龙, 江川, 骆名剑. ROC分析技术在机器学习中的应用[J]. 计算机工程与应用,2007,43(4):243 − 248. [ZHANG Xiaolong, JIANG Chuan, LUO Mingjian. Application of ROC analysis in machine learning[J]. Computer Engineering and Applications,2007,43(4):243 − 248. (in Chinese with English abstract)] Google Scholar ZHANG Xiaolong, JIANG Chuan, LUO Mingjian. Application of ROC analysis in machine learning［J］. Computer Engineering and Applications, 2007, 43（4）: 243 − 248. （in Chinese with English abstract） Google Scholar
[29]	RAHMATI O,TAHMASEBIPOUR N,HAGHIZADEH A,et al. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion[J]. Geomorphology,2017,298:118 − 137. doi: 10.1016/j.geomorph.2017.09.006 CrossRef Google Scholar
[30]	DI B,ZHANG H,LIU Y,et al. Assessing Susceptibility of Debris Flow in southwest China Using Gradient Boosting Machine[J]. Scientific Reports,2019,9:12532. doi: 10.1038/s41598-019-48986-5 CrossRef Google Scholar
[31]	GAROSI Y,SHEKLABADI M,POURGHASEMI H R,et al. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping[J]. Geoderma,2018,330:65 − 78. doi: 10.1016/j.geoderma.2018.05.027 CrossRef Google Scholar