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Discussion papers | Copyright
https://doi.org/10.5194/nhess-2017-325
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Oct 2017

Research article | 26 Oct 2017

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This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.

Bag-of-words-based anomaly-detection principal component analysis and stochastic optimization for debris flow detection and evacuation planning

Chia-Chun Kuo1, Yi-Ren Yeh2, Kuan-wen Chou3, Chien-Lin Huang3, and Ming-Che Hu1 Chia-Chun Kuo et al.
  • 1Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
  • 2Department of Mathematics, National Kaohsiung Normal University, No. 62, Shenjhong Road, Yanchao District, Kaohsiung, Taiwan
  • 3hetengtech Company Limited, Rm. 6A, 2F., No. 7, Sec. 3, New Taipei Blvd., Xinzhuang Dist., New Taipei City 242, Taiwan

Abstract. Debris flows are natural disasters, with soil mass, rocks, and water traveling down a mountainside slope. Debris flows are extremely dangerous; their occurrence incurs huge losses to life and property. The purpose of this research is to develop debris flow detection and emergency evacuation systems. A bag-of-words model is established for analyzing the features of debris flow events, and an anomaly-detection principal component analysis (PCA) model is proposed to detect debris flow. Using real-time debris flow prediction and monitoring, a stochastic optimization model for evacuation planning is formulated. Case studies of debris flow detection in Shenmu village and Fengchiu, central Taiwan, are conducted. Shenmu village and Fengchiu are areas of high potential debris flow, and each has a population of around 800 people. The results show that combining bag-of-words and anomaly-detection PCA methods could predict 6 out of 8 occurrences of actual events, providing a prediction rate of 75%. In addition, the models make 13 predictions, and 6 of them are correct, providing a prediction accuracy of 46%. Optimal parameters (including window size, bag length, filter ratio of training data, and anomaly threshold) of the models are also examined to increase the accuracy of debris flow prediction.

Chia-Chun Kuo et al.
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Status: closed (peer review stopped)
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Interactive discussion
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Chia-Chun Kuo et al.
Chia-Chun Kuo et al.
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Short summary
Debris flows are natural disasters, with soil mass, rocks, and water traveling down a mountainside slope. Debris flows are extremely dangerous; their occurrence incurs huge losses to life and property. The purpose of this research is to develop debris flow detection and emergency evacuation systems. Case studies of debris flow detection in Shenmu village and Fengchiu, central Taiwan, are conducted.
Debris flows are natural disasters, with soil mass, rocks, and water traveling down a...
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