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 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

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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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