Skip to main content

Advertisement

SpringerLink
Log in

Effects of reservoir water level fluctuations and rainfall on a landslide by two-way ANOVA and K-means clustering

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

The deformation and failure of reservoir landslides are typically affected by reservoir water level fluctuations, rainfall, and their jointed influence. This study develops a novel approach to interpret the effect of reservoir water level and precipitation and their interaction on the displacement of reservoir landslides. This new methodology, which integrates the two-way analysis of variance (ANOVA) and K-means clustering, can consider numerical dependent variables and the joint effect of two independent influence factors. The proposed methodology is illustrated using the Gapa landslide in Southwest China, an active reservoir landslide subjected to rainfall and an 80-m cyclic reservoir water level fluctuation. The mechanisms of the rapid deformation in the landslide are revealed using the proposed methodology by clustering both the reservoir water fluctuations and rainfall intensities into multiple phases and examine each combination using ANOVA. The results show that the landslide trend displacement rate during reservoir drawdown at low reservoir water level with heavy rainfall increases by 0.5–0.7 mm/day compared with reservoir drawdown at high reservoir water level, and increases by 1.2–1.8 mm/day compared with drawdown at low reservoir water level without rainfall. The reservoir filling at high water level is the safest phase, even with rain. The main findings by the method are confirmed by Pearson’s correlation and numerical tests. The proposed approach provides a satisfactory analytical method to identify the joint influence of reservoir water level and rainfall on the deformation of reservoir landslides at different time phases.

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
Fig. 13

Similar content being viewed by others

Abbreviations

ANOVA:

Analysis of variance

DHL:

Drawdown at high levels

DLL:

Drawdown at low levels

FHL:

Filling at high levels

FLL:

Filling at low levels

HR:

Heavy rain

IR:

Light rain

MR:

Medium rain

NR:

No rain

NWL:

Normal water levels

References

  • Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province. Iran CATENA 120:149–162

    Article  Google Scholar 

  • Armaş I (2012) Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania. Nat Hazards 60(3):937–950

  • Aubertin M, Mbonimpa M, Bussière B, Chapuis RP (2003) A model to predict the water retention curve from basic geotechnical properties. Can Geotech J 40(6):1104–1122

    Article  Google Scholar 

  • Brardinoni F, Slaymaker O, Hassan MA (2003) Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data. Geomorphology 54(3–4):179–196

    Article  Google Scholar 

  • Cascini L, Calvello M, Grimaldi GM (2010) Groundwater Modeling for the Analysis of Active Slow-Moving Landslides. Journal of Geotechnical and Geoenvironmental Engineering 136(9):1220–1230

    Article  Google Scholar 

  • Celebi ME, Aydin K (2016) Unsupervised learning algorithms [M]. Springer

    Book  Google Scholar 

  • Dai FC, Lee CF (2001) Frequency–volume relation and prediction of rainfall-induced landslides. Eng Geol 59(3):253–266

    Article  Google Scholar 

  • Du J, Yin K, Lacasse S (2013) Displacement prediction in colluvial landslides, Three Gorges Reservoir. China Landslides 10(2):203–218

    Article  Google Scholar 

  • Gao X, Liu H, Zhan, W, Wang W, Wang Z (2018) Influences of reservoir water level drawdown on slope stability and reliability analysis. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 13(2):145–153

  • Genevois R, Ghirotti M (2005) The 1963 vaiont landslide. Giornale Di Geologia Applicata 1(1):41–52

    Google Scholar 

  • GEO-SLOPE International Ltd (2007) Seepage and stability modeling with SEEP/W and SLOPE/W (users manuals). GEO-SLOPE International Ltd., Calgary, Canada

    Google Scholar 

  • Gorsevski PV, Gessler PE, Jankowski P (2003) Integrating a fuzzy k -means classification and a Bayesian approach for spatial prediction of landslide hazard. J Geogr Syst 5(3):223–251

    Article  Google Scholar 

  • Gu D, Huang D, Yang W, Zhu J, Fu G (2017) Understanding the triggering mechanism and possible kinematic evolution of a reactivated landslide in the Three Gorges Reservoir. Landslides 14(6):2073–2087

    Article  Google Scholar 

  • Gutiérrez F, Linares R, Roqué C, Zarroca M, Carbonel D, Rosell J, Gutiérrez M (2015) Large landslides associated with a diapiric fold in Canelles Reservoir (Spanish Pyrenees): Detailed geological–geomorphological mapping, trenching and electrical resistivity imaging. GEOMORPHOLOGY 241:224–242

  • Hu X, Zhang M, Sun M, Huang K, Song Y (2015) Deformation characteristics and failure mode of the Zhujiadian landslide in the Three Gorges Reservoir, China. Bull Eng Geol Env 74(1):1–12

    Article  Google Scholar 

  • Hu X, He C, Zhou C, Xu C, Zhang H, Wang Q, Wu S (2019) Model test and numerical analysis on the deformation and stability of a landslide subjected to reservoir filling. Geofluids 2019:5924580

    Article  Google Scholar 

  • Hu X, Wu S, Zhang G, Zheng W, Liu C, He C, Liu Z, Guo X, Zhang H (2021) Landslide displacement prediction using kinematics-based random forests method: A case study in Jinping Reservoir Area. China Engineering Geology 283:105975. https://doi.org/10.1016/j.enggeo.2020.105975

    Article  Google Scholar 

  • Huang Q, Wang J, Xue X (2016) Interpreting the influence of rainfall and reservoir infilling on a landslide. Landslides 13(5):1139–1149

    Article  Google Scholar 

  • Intrieri E, Carlà T, Gigli G (2019) Forecasting the time of failure of landslides at slope-scale: A literature review. EARTH-SCI REV 193:333-349. https://doi.org/10.1016/j.earscirev.2019.03.019

  • Iversen GR, Gergen M (1997) Statistics[M]

  • Jones FO, Embody DR, Peterson WL, Hazlewood RM (1961) Landslides along the Columbia River Valley, Northeastern Washington: descriptions of landslides and statistical analyses of data on some 200 landslides in pleistocene sediments. US Government Printing Office

  • Kalenchuk KS, Hutchinson DJ, Diederichs MS (2013) Downie Slide: numerical simulation of groundwater fluctuations influencing the behaviour of a massive landslide. Bull Eng Geol Env 72(3–4):397–412

    Article  Google Scholar 

  • Keefer DK (2000) Statistical analysis of an earthquake-induced landslide distribution — the 1989 Loma Prieta. California Event Engineering Geology 58(3):231–249

    Article  Google Scholar 

  • Kiang MY (2001) Extending the Kohonen self-organizing map networks for clustering analysis. Comput Stat Data Anal 38(2):161–180

    Article  Google Scholar 

  • B Li et al 2020 Mechanism of valley narrowing deformation during reservoir filling of a high arch dam Eur J Environ Civ Eng 1–11. https://doi.org/10.1080/19648189.2020.1763843

  • Li C, Tang H, Ge Y, Hu X, Wang L (2014) Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the Three Gorges Reservoir Region, China. Stoch Env Res Risk Assess 28(6):1465–1477

    Article  Google Scholar 

  • Lian C, Zeng Z, Yao W, Tang H (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stoch Env Res Risk Assess 28(8):1957–1972

    Article  Google Scholar 

  • Liao K, Wu Y, Miao F, Li L, Xue Y (2019) Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide. Bull Eng Geol Env 79(2):673–685

    Article  Google Scholar 

  • Liao K, Wu Y, Miao F, Li L, Xue Y (2021) Effect of weakening of sliding zone soils in hydro-fluctuation belt on long-term reliability of reservoir landslides. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-021-02167-9

  • Ma J, Tang H, Liu X, Hu X, Sun M, Song Y (2017) Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China. Landslides 14(3):1275–1281

  • Ma J, Tang H, Hu X, Bobet A, Zhang M, Zhu T, Song Y, Ez Eldin MAM (2017) Identification of causal factors for the Majiagou landslide using modern data mining methods. Landslides 14(1):311–322

    Article  Google Scholar 

  • Matsuura S, Asano S, Okamoto T (2008) Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide. Eng Geol 101(1–2):49–59

    Article  Google Scholar 

  • Miao F, Wu Y, Li L, Liao K, Xue Y (2020) Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods. Nat Hazards. https://doi.org/10.1007/s11069-020-04419-5

    Article  Google Scholar 

  • Riemer, W (1992) Landslides and reservoirs (keynote paper). In Proceedings of the 6th International Symposium on Landslides, vol 1. Christchurch: [sn], pp 373–2

  • Panizzo A, De Girolamo P, Di Risio M, Maistri A, Petaccia A (2005) Great landslide events in Italian artificial reservoirs. Nat Hazard 5(5):733–740

    Article  Google Scholar 

  • Paronuzzi P, Rigo E, Bolla A (2013) Influence of filling–drawdown cycles of the Vajont reservoir on Mt. Toc Slope Stability Geomorphology 191:75–93

    Article  Google Scholar 

  • Pinyol NM, Alonso EE, Corominas J, Moya J (2012) Canelles landslide: modeling rapid drawdown and fast potential sliding. Landslides 9:33–51

    Article  Google Scholar 

  • SAS Institute (1985) SAS user's guide: statistics, vol 2. Sas Inst

  • Tan F, Hu X, He C, Zhang Y, Zhang H, Zhou C, Wang Q (2018) Identifying the Main Control Factors for Different Deformation Stages of Landslide. Geotech Geol Eng 36(1):469–482

    Article  Google Scholar 

  • Tang H, Wasowski J, Juang C (2019) Geohazards in the three Gorges Reservoir Area, China-Lessons learned from decades of research. Eng Geol 2019:105267

    Article  Google Scholar 

  • Tomás R, Li Z, Lopez-Sanchez JM, Liu P, Singleton A (2016) Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide. Landslides 13(3):437–450

    Article  Google Scholar 

  • Walker LR (1994) Effects of fern thickets on woodland development on landslides in Puerto Rico. J Veg Sci 5(4):525–532

    Article  Google Scholar 

  • Wang F, Zhang Y, Huo Z, Matsumoto T, Huang B (2004) The July 14, 2003 Qianjiangping landslide, Three Gorges Reservoir. China Landslides 1(2):157–162

    Google Scholar 

  • Wang F, Li T (eds) (2009) Landslide Disaster Mitigation in Three Gorges Reservoir, China. Springer, Berlin

    Google Scholar 

  • Wei Z, Sun H, Xu H, Wu G, Xie W (2019) The effects of rainfall regimes and rainfall characteristics on peak discharge in a small debris flow-prone catchment. J Mt Sci 16(7):1646–1660

    Article  Google Scholar 

  • Wolter A, Stead D, Ward BC, Clague JJ, Ghirotti M (2016) Engineering geomorphological characterisation of the Vajont Slide, Italy, and a new interpretation of the chronology and evolution of the landslide. Landslides 13(5):1067–1081

    Article  Google Scholar 

  • Wu Q, Tang H, Ma X, Wu Y, Hu X, Wang L, Criss R, Yuan Y, Xu Y(2019) Identification of movement characteristics and causal factors of the Shuping landslide based on monitored displacements.B ENG GEOL ENVIRON 78 (3):2093-2106. https://doi.org/10.1007/s10064-018-1237-2

  • Wu Y, Miao F, Li L, Xie Y, Chang B (2017) Time-varying reliability analysis of Huangtupo Riverside No.2 Landslide in the Three Gorges Reservoir based on water-soil coupling. Eng Geol 226:267–276

    Article  Google Scholar 

  • Yao W, Li C, Zuo Q, Zhan H, Criss RE (2019) Spatiotemporal deformation characteristics and triggering factors of Baijiabao landslide in Three Gorges Reservoir region, China. Geomorphology 343:34–47

    Article  Google Scholar 

  • Yin Y, Huang B, Wang W, Wei Y, Ma X, Ma F, Zhao C (2016) Reservoir-induced landslides and risk control in Three Gorges Project on Yangtze River, China. Journal of Rock Mechanics and Geotechnical Engineering 8(5):577–595

    Article  Google Scholar 

  • Zangerl C, Eberhardt E, Perzlmaier S (2010) Kinematic behaviour and velocity characteristics of a complex deep-seated crystalline rockslide system in relation to its interaction with a dam reservoir. Eng Geol 112(1–4):53–67

    Article  Google Scholar 

  • Zhu D, Lee C, Qian Q, Chen G (2005) A concise algorithm for computing the factor of safety using the Morgenstern-Price method. Can Geotech J 42(1):272–278

    Article  Google Scholar 

Download references

Acknowledgements

The first author appreciates the support for field monitoring and data provided by Yalong River Hydropower Development Company Limited and Chengdu Engineering Corporation Limited.

Funding

This study is funded by the National Key Research and Development Program of China (No. 2017YFC1501302), the Key Program of National Natural Science Foundation of China (No. 41630643), the Fundamental Research Funds for the Central Universities, and China University of Geosciences (Wuhan) (No. CUGCJ1701).

Author information

Authors and Affiliations

  1. Faculty of EngineeringChina, University of Geosciences, Wuhan, Hubei, 430074, People’s Republic of China

    Shuangshuang Wu, Xinli Hu, Guangcheng Zhang, Han Zhang & Xuan Wang

  2. School of Engineering, University of Northern British Columbia, Prince George, BC, V2N 4Z9, Canada

    Wenbo Zheng

  3. School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan, Guangdong, 523808, People’s Republic of China

    Chuncan He

Authors
  1. Shuangshuang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinli Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbo Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chuncan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangcheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Han Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinli Hu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, S., Hu, X., Zheng, W. et al. Effects of reservoir water level fluctuations and rainfall on a landslide by two-way ANOVA and K-means clustering. Bull Eng Geol Environ 80, 5405–5421 (2021). https://doi.org/10.1007/s10064-021-02273-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-021-02273-8

Keywords

Search

Navigation