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A Logistic Model Predicting Occurrence Probability of Debris Flow

Received: 17 January 2019     Published: 28 April 2019
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Abstract

This paper presents a logistic model for predicting the occurrence probability of debris flows based on rainfall intensity and duration. The data from a total of 354 rainfall events were used to calibrate the model, among which 249 were triggering a debris flow while 105 were not. The model will be useful to the decision making of debris flow early warning in the future. That is, given the estimated occurrence probability = 70% subject to a combination of rainfall intensity and duration, there is a 30% probability that the early warning will be a false alarm. By contrast, if decision makers decide not to issue an early warning, then there is a 70% chance leading to a missed alarm. Subsequently, integrating the consequences of missed alarm and false alarm into the equation, the respective risks can be computed, based on which decision makers can make a more robust decision whether an early warning is needed or not by choosing the scenario with a lower risk.

Published in American Journal of Civil Engineering (Volume 7, Issue 1)
DOI 10.11648/j.ajce.20190701.14
Page(s) 21-26
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Debris Flow, Logistic Regression, Occurrence Probability

References
[1] Guo XJ, Cui P, Li Y, Ge Y, Mahoney WB (2016) Intensity-duration threshold of rainfall-triggered debris flows in the Wenchuan Earthquake affected area, China. Geomorphology 253: 208–216.
[2] Wu MH, Wang JP, Chen IC (2018) Optimization approach for determining rainfall duration-intensity thresholds for debris flow forecasting. Bulletin of Engineering Geology and the Environment pp 1–7.
[3] Tang H, Liu Y, He SM, Chen Z, Wang DP (2018) Measuring and estimating the impact pressure of debris flows on bridge piers based on large-scale laboratory experiments. Landslides July 2018, Volume 15, Issue 7, pp 1331–1345.
[4] Lai SY, Chang WJ, Lin PS (2006) Logistic Regression Model for Evaluating Soil Liquefaction Probability Using CPT Data Journal of Geotechnical and Geoenvironmental Engineering Volume 132 Issue 6, Pages 694-704.
[5] Chang KT, Chiang SH, Hsu ML (2007) Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology Volume 89, Issues 3–4, Pages 335-347.
[6] Dahal R, Hasegawa S (2008) Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology 100:429–443.
[7] Gabet E, Burbank D, Putkonen J, Pratt-Sitaula B, Ojha T (2004) Rainfall thresholds for landsliding in the Himalayas of Nepal. Geomorphology 63:131–143.
[8] Guo XJ, Cui P, Li Y (2013) Debris flow warning threshold based on antecedent rainfall: a case study in Jiangjia Ravine, Yunnan, China. J. Mt. Sci. 10(2):305–314.
[9] Aleotti P (2004) A warning system for rainfall-induced shallow failures. Eng. Geol. 73:247–265.
[10] Guzzetti F, Peruccacci S, Rossi M, Stark C (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorog. Atmos. Phys. 98:239–2125.
[11] Guzzetti F, Peruccacci S, Rossi M, Stark C (2008) The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides 5:3–17.
[12] Hong Y, Hiura H, Shino K, Sassa K, Suemine A, Fukuoka H, Wang G (2005) The influence of intense rainfall on the activity of large-scale crystalline schist landslides in Shikoku Island, Japan. Landslides 2:97–105.
[13] Iverson RM (1997) The physics of debris flows. Rev Geophys 35(3): 245–296.
[14] Jibson R (1989) Debris flow in southern Puerto Rico. Geol. Soc. Am. Spec. Pap. 236:29–55.
[15] Larsen M, Simon A (1993) A rainfall intensity-duration threshold for landslides in a humid-tropical environment, Puerto Rico. Geografiska Ann. Series A, Phys. Geography 75:13–23.
[16] Mileti SM, Peek L (2000) The social psychology of public response to warnings of a nuclear power plant accident. Journal of Hazardous Materials 75: 181–194.
[17] Saito S, Daichi N, Hiroshi M (2010) Relationship between the initiation of a shallow landslide and rainfall intensity–duration thresholds in Japan. Geomorphology 118:1125–1175.
[18] Tang C, Van AT, Chang M, Chen GQ, Zhao XH, Huang XC (2012) Catastrophic debris flows on 13 August 2010 in the Qingping area, southwestern China: The combined effects of a strong earthquake and subsequent rainstorms. Geomorphology 139-140:559–576.
[19] Tang XS, Wang JP, Yang W, Li DQ (2018) Joint probability modeling for two debris-flow variables: copula approach. Natural Hazards Review ASCE 19(2): 05018004.
[20] Wang JP, Wu YM, Lin TL, Brant L (2012) The uncertainties of a Pd3-PGV onsite earthquake early warning system. Soil Dynamics and Earthquake Engineering 36: 32–37.
[21] Xu Y, Wang JP, Wu YM, Kuochen H (2017) Reliability assessment on earthquake early warning: A case study from Taiwan. Soil Dynamics and Earthquake Engineering 92: 397–407.
[22] Zhou W, Tang C (2013) Rainfall thresholds for debris flow initiation in the Wenchuan Earthquake-stricken area, southwestern China. Landslides 11(5): 877–887.
[23] van Asch, T. W. J., Tang, C., Alkema, D., Zhu, J., Zhou, W., (2014). An integrated model to assess critical rainfall thresholds for run-out distances of debris flows. Nat. Hazards 70 (1), 299–311.
[24] Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., Guzzetti, F., (2010). Rainfall thresholds for the possible occurrence of landslides in Italy. Nat. Hazards Earth Syst. Sci. 10, 447–458.
[25] Chen, Y., Booth, D. C., (2011). The Wenchuan Earthquake of 2008. Science Press, Beijing.
[26] Chen, X. C., You, Y., Liu, J. F., Chen, H., (2011). Characteristics and discrimination of debris flows following Wenchuan Earthquake in Qianfoshan scenic spot of Anxian County, Sichuan Province, China. Sci. Geogr. Sin. 31 (12), 1500–1505 (in Chinese).
[27] Cui, P., (1992). Studies on condition and mechanism of debris flow initiation by means of experiment. Chin. Sci. Bull. 37 (9), 759–763.
[28] Cui, P., Zhu, Y. Y., Chen, J., Han, Y. S., Liu, H. J., 2007. Relationships between antecedent rainfall and debris flows in jiangjia ravine, China. In: Chen, C. L., Major, J. J. (Eds.), DebrisFlow Hazard Mitigation-Mechanics, Prediction, and Assessment. Millpress, Rotterdam, pp. 1–10.
[29] Cui, P., Zou, Q., Xiang, L. Z., Zeng, C., 2013. Risk assessment of simultaneous debris flows in mountain townships. Prog. Phys. Geogr. 37 (4), 516–542.
[30] Dai, F. C., Xu, C., Yao, X., Xu, L., Tu, X. B., Gong, Q. M., 2011. Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake, China. J. Asian Earth Sci. 40, 883–895.
[31] Caine N (1980) The rainfall intensity–duration control of shallow landslides and debris flows. Geografiska Annaler: Series A Phys. Geogr. 62:23–27.
[32] Cannon S, Gartner J, Wilson R, Bowers J, Laber J (2008) Storm rainfall conditions for floods and debris flows from recently burned areas in southwestern Colorado and southern California. Geomorphology 96:250–269.
Cite This Article
  • APA Style

    J. P. Wang, Yijie Wu. (2019). A Logistic Model Predicting Occurrence Probability of Debris Flow. American Journal of Civil Engineering, 7(1), 21-26. https://doi.org/10.11648/j.ajce.20190701.14

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    ACS Style

    J. P. Wang; Yijie Wu. A Logistic Model Predicting Occurrence Probability of Debris Flow. Am. J. Civ. Eng. 2019, 7(1), 21-26. doi: 10.11648/j.ajce.20190701.14

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    AMA Style

    J. P. Wang, Yijie Wu. A Logistic Model Predicting Occurrence Probability of Debris Flow. Am J Civ Eng. 2019;7(1):21-26. doi: 10.11648/j.ajce.20190701.14

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  • @article{10.11648/j.ajce.20190701.14,
      author = {J. P. Wang and Yijie Wu},
      title = {A Logistic Model Predicting Occurrence Probability of Debris Flow},
      journal = {American Journal of Civil Engineering},
      volume = {7},
      number = {1},
      pages = {21-26},
      doi = {10.11648/j.ajce.20190701.14},
      url = {https://doi.org/10.11648/j.ajce.20190701.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20190701.14},
      abstract = {This paper presents a logistic model for predicting the occurrence probability of debris flows based on rainfall intensity and duration. The data from a total of 354 rainfall events were used to calibrate the model, among which 249 were triggering a debris flow while 105 were not. The model will be useful to the decision making of debris flow early warning in the future. That is, given the estimated occurrence probability = 70% subject to a combination of rainfall intensity and duration, there is a 30% probability that the early warning will be a false alarm. By contrast, if decision makers decide not to issue an early warning, then there is a 70% chance leading to a missed alarm. Subsequently, integrating the consequences of missed alarm and false alarm into the equation, the respective risks can be computed, based on which decision makers can make a more robust decision whether an early warning is needed or not by choosing the scenario with a lower risk.},
     year = {2019}
    }
    

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    T1  - A Logistic Model Predicting Occurrence Probability of Debris Flow
    AU  - J. P. Wang
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    T2  - American Journal of Civil Engineering
    JF  - American Journal of Civil Engineering
    JO  - American Journal of Civil Engineering
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ajce.20190701.14
    AB  - This paper presents a logistic model for predicting the occurrence probability of debris flows based on rainfall intensity and duration. The data from a total of 354 rainfall events were used to calibrate the model, among which 249 were triggering a debris flow while 105 were not. The model will be useful to the decision making of debris flow early warning in the future. That is, given the estimated occurrence probability = 70% subject to a combination of rainfall intensity and duration, there is a 30% probability that the early warning will be a false alarm. By contrast, if decision makers decide not to issue an early warning, then there is a 70% chance leading to a missed alarm. Subsequently, integrating the consequences of missed alarm and false alarm into the equation, the respective risks can be computed, based on which decision makers can make a more robust decision whether an early warning is needed or not by choosing the scenario with a lower risk.
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Author Information
  • Civil Engineering, National Central University, Taoyuan, Taiwan, Republic of China

  • Civil Engineering, National Central University, Taoyuan, Taiwan, Republic of China

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