A Logistic Model Predicting Occurrence Probability of Debris Flow

J. P. Wang; Yijie Wu

doi:doi:10.11648/j.ajce.20190701.14

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A Logistic Model Predicting Occurrence Probability of Debris Flow

J. P. Wang, Yijie Wu

Published in American Journal of Civil Engineering (Volume 7, Issue 1)

Received: 17 January 2019 Accepted: 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), 2024. 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.

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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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TY  - JOUR
T1  - A Logistic Model Predicting Occurrence Probability of Debris Flow
AU  - J. P. Wang
AU  - Yijie Wu
Y1  - 2019/04/28
PY  - 2019
N1  - https://doi.org/10.11648/j.ajce.20190701.14
DO  - 10.11648/j.ajce.20190701.14
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 21
EP  - 26
PB  - Science Publishing Group
SN  - 2330-8737
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.
VL  - 7
IS  - 1
ER  -

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Author Information

J. P. Wang

Civil Engineering, National Central University, Taoyuan, Taiwan, Republic of China
Yijie Wu

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

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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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - A Logistic Model Predicting Occurrence Probability of Debris Flow
AU  - J. P. Wang
AU  - Yijie Wu
Y1  - 2019/04/28
PY  - 2019
N1  - https://doi.org/10.11648/j.ajce.20190701.14
DO  - 10.11648/j.ajce.20190701.14
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 21
EP  - 26
PB  - Science Publishing Group
SN  - 2330-8737
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.
VL  - 7
IS  - 1
ER  -

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