Skip to main content

Advertisement

SpringerLink
Log in

Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Blikra LH, Nemec W (1998) Postglacial colluvium in western Norway: depositional processes, facies and palaeoclimatic record. Sedimentology 45(5):909–959

    Article  Google Scholar 

  • Carrivick JL, Rushmer EL (2006) Understanding high-magnitude outburst floods. Geol Today 22:60–65

    Article  Google Scholar 

  • Carrivick JL (2010) Dam break—outburst flood propagation and transient hydraulics: a geosciences perspective. J Hydrol 380:338–355

    Article  Google Scholar 

  • Carrivick JL, Jones R, Keevil G (2011) Experimental insights on geomorphological processes within dam break outburst floods. J Hydrol 408:153–163

    Article  Google Scholar 

  • Casagli N, Ermini L, Rosati G (2003) Determining grain size distribution of material composing landslide dams in the Northern Apennine: sampling and processing methods. Eng Geol 69:83–97

    Article  Google Scholar 

  • Chang FJ, Hwang YY (1999) A self-organization algorithm for real-time flood forecast. Hydrol Process 13(2):123–138

    Article  Google Scholar 

  • Chang FJ, Liang JM, Chen YC (2001) Flood forecasting using radial basis function neural networks. IEEE Trans on Syst, Man, and Cybern—Part C: Appl Rev 31(4):530–535

    Article  Google Scholar 

  • Chang TJ, Kao HM, Chang KH, Hsu MH (2011) Numerical simulation of shallow-water dam break flows in open channels using smoothed particle hydrodynamics. J Hydrol 408:78–90

    Article  Google Scholar 

  • Chen CS, Chou FNF, Chen BPT (2010a) Spatial information-based back-propagation neural network modeling for outflow estimation of ungauged catchment. Water Resour Manag 24(14):4175–4197

    Article  Google Scholar 

  • Chen CS, Chen BP-T, Chou FNF, Yang CC (2010b) Development and application of a decision group back-propagation neural network for flood forecasting. J Hydrol 385(1–4):173–182

    Article  Google Scholar 

  • Cheng SP (2010) The oral history of a disaster at the Hsiaolin Village. National Taiwan Museum, Taipei, p 246

    Google Scholar 

  • Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37:885–896

    Article  Google Scholar 

  • Costa JE, Schuster RL (1988) The formation and failure of natural dams. Geol Soc Am Bull 100:1054–1068

    Article  Google Scholar 

  • Cui P, Zhu Y, Han Y, Chen X, Zhuang J (2009) The 12 May Wenchuan earthquake-induced landslide lakes: distribution and preliminary risk evaluation. Landslides 6(3):209–223

    Article  Google Scholar 

  • Davis WM (1882) On the classification of lake basins: proceedings. Boston Soc Nat Hist 21:315–381

    Google Scholar 

  • Dong JJ, Li YS, Kuo CY, Sung RT, Li MH, Lee CT, Chen CC, Lee WR (2011) The formation and breach of a short-lived landslide dam at Hsiaolin Village, Taiwan—part I: Post-event reconstruction of dam geometry. Eng Geol 123(1–2):40–59

    Article  Google Scholar 

  • Dong JJ, Lai PJ, Chang CP, Yang SH, Yeh KC, Liao JJ, Pan YW (2013) Deriving landslide dam geometry from remote sensing images for the rapid assessment of critical parameters related to dam-breach hazards. Landslides. doi:10.1007/s10346-012-0375-z

  • Evans SG, Hungr O (1993) The assessment of rockfall hazard at the base of talus slopes. Can Geotech J 30:620–636

    Article  Google Scholar 

  • Fan X, Tang CX, van Westen CJ, Alkema D (2012) Simulating dam-breach flood scenarios of the Tangjiashan landslide dam induced by the Wenchuan earthquake. Nat Hazards Earth Syst Sci 12(10):3031–3044

    Article  Google Scholar 

  • Feng ZY (2011) The seismic signatures of the 2009 Shiaolin landslide in Taiwan. Nat Hazards Earth Syst Sci 11(5):1559–1569

    Article  Google Scholar 

  • Garcia-Navarro P, Villanueva AFI (1999) Dam-break flow simulation: some results for one-dimensional models of real cases. J Hydrol 216:227–247

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Hsu KL, Gupta VK, Sorooshian S (1995) Artificial neural network modelling of the rainfall–runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Hutchinson GE (1957) A treatise on limnology, vol. 1. Wiley, New York, p 1015

    Google Scholar 

  • Kim G, Barros AP (2001) Quantitative flood forecasting using multisensor data and neural networks. J Hydrol 246(1–4):45–62

    Article  Google Scholar 

  • Kuo Y-S, Tsai Y-J, Chen Y-SM, Shieh C-L, Miyamoto K, Itoh T (2012) Movement of deep-seated rainfall-induced landslide at Shiao Lin Village during Typhoon Morakot. Landslides 10(2):191–202

    Article  Google Scholar 

  • Li MH, Sung RT, Dong JJ, Lee CT, Chen CC (2011) The formation and breaching of a short lived landslide Dam at Hsiaolin Village, Taiwan—part II: Simulation of debris flow with landslide dam breach. Eng Geol 123(1–2):60–71

    Article  Google Scholar 

  • Lo CM, Lin ML, Tang CL, Hu JC (2011) A kinematic model of the Hsiaolin landslide calibrated to the morphology of the landslide deposit. Eng Geol 123:22–39

    Article  Google Scholar 

  • Meyer W, Schuster RL, Sabol MA (1994) Potential for seepage erosion of landslide dam. J Geotech Eng ASCE 120(7):1211–1229

    Article  Google Scholar 

  • Minns AW, Hall MJ (1996) Artificial neural network as rainfall–runoff models. Hydrol Sci J 41(3):399–419

    Article  Google Scholar 

  • Mizuyama T, Satohuka Y, Ogawa K, Mori T (2006) Estimating the outflow discharge rate from landslide dam outbursts, disaster mitigation of debris flows. Slope Failures and Landslides 1:365–377

    Google Scholar 

  • Muftuoglu RF (1991) Monthly runoff generation by non-linear models. J Hydrol 125:277–291

    Article  Google Scholar 

  • Nemec W, Kazanci N (1999) Quaternary colluvium in west-central Anatolia: sedimentary facies and palaeoclimatic significance. Sedimentology 46(1):139–170

    Article  Google Scholar 

  • Peng M, Zhang LM (2012a) Analysis of human risks due to dam break floods—part 1: A new model based on Bayesian networks. Nat Hazards 64(2):1899–1923

    Article  Google Scholar 

  • Peng M, Zhang LM (2012b) Analysis of human risk due to dam break floods—part 2: Application to Tangjiashan landslide dam failure. Nat Hazards 64(1):903–933

    Article  Google Scholar 

  • Peng M, Zhang LM (2013) Dynamic decision making for dam-break emergency management—part 1: Theoretical framework. Nat Hazards Earth Syst Sci 13:425–437

    Google Scholar 

  • Peng M, Zhang LM (2013) Dynamic decision making for dam-break emergency management—part 2: Application to Tangjiashan landslide dam failure. Nat Hazards Earth Sys Sci 13:439–454

    Google Scholar 

  • Prestininzi P (2008) Suitability of the diffusive model for dam break simulation: application to a CADAM experiment. J Hydrol 361:172–185

    Article  Google Scholar 

  • Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall–runoff model using an artificial neural network. J Hydrol 216(1–2):32–55

    Article  Google Scholar 

  • Shamseldin AY (1997) Application of neural network technique to rainfall–runoff modelling. J Hydrol 199:272–294

    Article  Google Scholar 

  • Shieh CL, Wang CM, Lai WC, Tsang YC, Lee SP (2009) The composite hazard resulted from Typhoon Morakot in Taiwan. J Japan Soc Erosion Control Eng 62(4):61–65

    Google Scholar 

  • Singh VP, Snorrason A (1984) Sensitivity of outflow peak and flood stage to the selection of dam breach parameters and simulation models. J Hydrol 68(1–4):295–310

    Article  Google Scholar 

  • Swanson FJ, Oyagi N, Tominaga M (1986) Landslide dam in Japan. Landslide Dam: Processes Risk and Mitigation. Geotechnical Special Publication (ASCE), 3, pp 131–145

  • Tingsanchali T, Gautam MR (2000) Application of tank, NAM, ARMA and neural network models to flood forecasting. Hydrol Process 14(14):2473–2487

    Article  Google Scholar 

  • Takahashi T, Kuang SF (1988) Hydrograph prediction of debris flow due to failure of landslide dam. Annuals of Disaster Prevention Research Institute, Kyoto University 31(2):601–615

    Google Scholar 

  • Tsou CY, Feng ZY, Chigira M (2011) Catastrophic landslide induced by Typhoon Morakot, Shiaolin, Taiwan. Geomorphology 127(3–4):166–178

    Article  Google Scholar 

  • Waythomas CF (2001) Formation and failure of volcanic debris dams in the Chakachatna River valley associated with eruptions of the Spurr volcanic complex, Alaska. Geomorphology 39:111–129

    Article  Google Scholar 

  • Yang SH, Pan YW, Dong JJ, Yeh KC, Liao JJ (2012) A systematic approach for the assessment of flooding hazard and risk associated with a landslide dam. Nat Hazards 65:41–62

    Article  Google Scholar 

Download references

Acknowledgment

The water level data and the rating curve of the Shan-Lin Bridge river gauge are provided by the Water Resources Agency, Ministry of Economic Affairs in Taiwan. Part of this study was supported by the National Science Council, Taiwan. The project name is Modeling of The Compound Disaster in Hsiaolin Village (NSC 101-2218-E-006-001).

Author information

Authors and Affiliations

  1. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, No. 1, University Road, Tainan City, 701, Taiwan, Republic of China

    S.-P. Lee, C.-L. Shieh & Y.-S. Kuo

  2. Department of Civil Engineering, National Taipei University of Technology, No. 1, Sec. 3., Zhongxiao East Road, Taipei, 106, Taiwan, Republic of China

    Y.-C. Chen

Authors
  1. S.-P. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Y.-C. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C.-L. Shieh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y.-S. Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y.-S. Kuo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, SP., Chen, YC., Shieh, CL. et al. Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam. Landslides 11, 1007–1017 (2014). https://doi.org/10.1007/s10346-013-0441-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-013-0441-1

Keywords

Search

Navigation