Natural Hazards

, Volume 67, Issue 2, pp 783–796 | Cite as

Application of a feature-based approach to debris flow detection by numerical simulation

  • Chih-Wei Lin
  • Cheng-Wu Chen
  • Wen-Ko Hsu
  • Chia-Yen Chen
  • Chung-Hung Tsai
  • Yi-Ping Hung
  • Wei-Ling Chiang
Original Article


A debris flow is a serious natural disaster which can occur anywhere whether in a valley or on a mountain slope, destroying everything it passes through. Debris flows can occur suddenly and cause residents in the path to suffer casualties and property loss. An early warning system is necessary to reduce the damage in order to protect human life and personal property. However, most debris flow detection systems, like wireless sensors, satellite images and radar, are not suitable for general public use. Vision surveillance systems are generally erected in Taiwan as public devices for security. Therefore, we propose a novel debris early warning system that uses a computer vision technique and build a simulation environment to prove the feasibility.


Debris flow Feature based Computer vision 



The authors would like to thank the National Science Council of the Republic of China, Taiwan, for their financial support of this research under Contract Nos. NSC100-2218-E-008-007, 100-2221-E-022-013-MY2 and 100-2628-E-022-002-MY2.


  1. Bay H, Tuytelaars T, & Van Gool L (2006) Surf: speeded up robust features. European Conference on computer vision, 404–417Google Scholar
  2. Chen CW (2004) Stability analysis of T-S fuzzy models for nonlinear multiple time-delay interconnected systems. Math Comput Simul 66:523–537CrossRefGoogle Scholar
  3. Chen CW (2006) Stability conditions of fuzzy systems and its application to structural and mechanical systems. Adv Eng Softw 37:624–629CrossRefGoogle Scholar
  4. Chen CW (2011) Stability analysis and robustness design of nonlinear systems: an NN-based approach. Appl Soft Comput 11(2):2735–2742CrossRefGoogle Scholar
  5. Chen CY (2012a) Assessment of the major hazard potential of interfacial solitary waves moving over a trapezoidal obstacle on a horizontal plateau. Nat Hazards 62(3):841–852CrossRefGoogle Scholar
  6. Chen CY (2012b) Disaster prevention and reduction for exploring teachers’ technology acceptance using a virtual reality system and partial least squares techniques. Nat Hazards 62(3):1217–1231CrossRefGoogle Scholar
  7. Chen KW, Lin CW, Chen MYY, Hung YP (2010) e-Fovea: a multi-resolution approach with steerable focus to large-scale and high-resolution monitoring. Paper presented at the Proceedings of the international conference on Multimedia, Firenze, ItalyGoogle Scholar
  8. Chen KW, Lin CW, Chiu TH, Chen MYY, Hung YP (2011) Multi-resolution design for large-scale and high-resolution monitoring. Multimedia, IEEE Trans on 13(6):1256–1268. doi: 10.1109/TMM.2011.2165055 CrossRefGoogle Scholar
  9. Cho CY, Chou PH, Chung YC, King CT, Tsai MJ, Lee BJ, & Chou TY (2008) Wireless Sensor Networks for Debris Flow Observation. Paper presented at the Sensor, Mesh and Ad Hoc Communications and Networks, 2008. SECON ‘08. 5th Annual IEEE Communications Society Conference onGoogle Scholar
  10. Hsiao FH, Chen CW, Liang YW, Xu SD, Chiang WL (2005a) T-S fuzzy controllers for nonlinear interconnected systems with multiple time delays. IEEE Trans Circuits Syst I Regul Pap 52:1883–1893CrossRefGoogle Scholar
  11. Hsiao FH, Hwang JD, Chen CW, Tsai ZR (2005b) Robust stabilization of nonlinear multiple time-delay large-scale systems via decentralized fuzzy control. IEEE Trans Fuzzy Sys 13:152–163CrossRefGoogle Scholar
  12. Hsu WK, Huang PC, Chang CC, Chen CW, Hung DM, Chiang WL (2011) An integrated flood risk assessment model for property insurance industry in Taiwan. Nat Hazards 58(3):1295–1309. doi: 10.1007/s11069-011-9732-9 CrossRefGoogle Scholar
  13. Hsu WK, Tseng CP, Chiang WL, Chen CW (2012) Risk and uncertainty analysis in the planning stages of a risk decision-making process. Nat Hazards 61(3):1355–1365. doi: 10.1007/s11069-011-0032-1 CrossRefGoogle Scholar
  14. Jin YQ, Xu F (2011) Monitoring and Early Warning the Debris Flow and Landslides Using VHF Radar Pulse Echoes From Layering Land Media. Geosci Remote Sens Lett IEEE 8(3):575–579. doi: 10.1109/LGRS.2010.2093598 CrossRefGoogle Scholar
  15. Lee HC, Cho CY, King CT, Fang YM, & Lee BJ (2009) Design and implementation of non-autonomous mobile wireless sensor for debris flow monitoring. Paper presented at the Mobile Adhoc and Sensor Systems, 2009. MASS ‘09. IEEE 6th International Conference onGoogle Scholar
  16. Lee HC, Banerjee A, Fang YM, Lee BJ, King CT (2010) Design of a multifunctional wireless sensor for in situ monitoring of debris flows. IEEE Trans Instrum Meas IEEE Trans on 59(11):2958–2967. doi: 10.1109/TIM.2010.2046361 CrossRefGoogle Scholar
  17. Lin JW (2012a) Kalman filter decision systems for debris flow hazard assessment. Nat Hazards 60(3):1255–1266CrossRefGoogle Scholar
  18. Lin JW (2012b) Modeling and assessment of bridge structure for seismic hazard prevention. Nat Hazards 61(3):1115–1126CrossRefGoogle Scholar
  19. Lin JW (2012c) Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Nat Hazards. doi: 10.1007/s11069-012-0236-z Google Scholar
  20. Lin CW, Hung YP, Hsu WK, Chiang WL, Chen CW (2013) The construction of a high-resolution visual monitoring for hazard analysis. Nat Hazards 65(3):1285–1292. doi: 10.1007/s11069-012-0409-9 CrossRefGoogle Scholar
  21. Lugeri N, Kundzewicz Z, Genovese E, Hochrainer S, Radziejewski M (2010) River flood risk and adaptation in Europe—assessment of the present status. Mitig Adapt Strateg Glob Chang 15(7):621–639. doi: 10.1007/s11027-009-9211-8 CrossRefGoogle Scholar
  22. Markus AA, Courage WMG, van Mierlo MCLM (2010) A computational framework for flood risk assessment in The Netherlands. Sci Program 18(2):93–105. doi: 10.3233/SPR-2010-0298 Google Scholar
  23. Pandey A, Singh S, Nathawat M (2010) Waterlogging and flood hazards vulnerability and risk assessment in Indo Gangetic plain. Nat Hazards 55(2):273–289. doi: 10.1007/s11069-010-9525-6 CrossRefGoogle Scholar
  24. Rau JY, Chen LC, Liu JK, Wu TH (2007) Dynamics monitoring and disaster assessment for watershed management using time-series satellite images. Geosci Remote Sens IEEE Trans on 45(6):1641–1649. doi: 10.1109/TGRS.2007.894928 CrossRefGoogle Scholar
  25. Shih BY (2012) Using Lego NXT to explore scientific literacy in disaster prevention and rescue systems. Nat Hazards. doi: 10.1007/s11069-012-0233-2 Google Scholar
  26. Soil and Water Conservation Bureau, C. o. A., Executive Yuan. Debris Flow disaster prevention information. available in
  27. Tang L, Hu DY, Li XJ, & Lian J (2010) Change detection of landslides and debris in south Taiwan after “Morakot” typhoon based on HJ-1-B Satellite images. Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE InternationalGoogle Scholar
  28. Tsai CH (2010) An earthquake disaster management mechanism based on risk assessment information for the tourism industry-A case study from the island of Taiwan. Tour Manag 31(4):470–481CrossRefGoogle Scholar
  29. Tsai CH (2011a) Development of a mechanism for typhoon and flood risk assessment and disaster management in the hotel industry—a case study of the Hualien area. Scand J Hosp Tour 11(3):324–341CrossRefGoogle Scholar
  30. Tsai CH (2011b) The establishment of a rapid natural disaster risk assessment model for the tourism industry. Tour Manag 32(1):158–171CrossRefGoogle Scholar
  31. Tseng CP (2011) A new viewpoint on risk control decision models for natural disasters. Nat Hazards 59(3):1715–1733CrossRefGoogle Scholar
  32. Tseng CP (2012a) Default risk-based probabilistic decision model for risk management and control. Nat Hazards. doi: 10.1007/s11069-012-0183-8 Google Scholar
  33. Tseng CP (2012b) Natural disaster management mechanisms for probabilistic earthquake loss. Nat Hazards 60(3):1055–1063CrossRefGoogle Scholar
  34. Tseng CP, Chen CW (2012) Natural disaster management mechanisms for probabilistic earthquake loss. Nat Hazards 60(3):1055–1063CrossRefGoogle Scholar
  35. Yang HC (2012) Potential hazard analysis from the viewpoint of flow measurement in large open-channel junctions. Nat Hazards 61(2):803–813CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Chih-Wei Lin
    • 1
    • 7
  • Cheng-Wu Chen
    • 3
  • Wen-Ko Hsu
    • 4
  • Chia-Yen Chen
    • 5
  • Chung-Hung Tsai
    • 6
  • Yi-Ping Hung
    • 1
    • 2
  • Wei-Ling Chiang
    • 7
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Graduate Institute of Networking and MultimediaNational Taiwan UniversityTaipeiTaiwan
  3. 3.Department of Maritime Information and TechnologyNational Kaohsiung Marine UniversityKaohsiungTaiwan
  4. 4.Research Center for Hazard Mitigation and PreventionNational Central UniversityTaoyuanTaiwan
  5. 5.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan
  6. 6.Department of Leisure Business ManagementNational Pingtung Institute of CommercePingtungTaiwan
  7. 7.Department of Civil EngineeringNational Central UniversityTaoyuanTaiwan, ROC

Personalised recommendations