Skip to main content

Advertisement

SpringerLink
Log in

Early prediction framework for a rainfall-induced landslide: validation through a real case study

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Shallow landslide occurrence is most common in rainy season in the form of flow of debris, costs heavy damages to the infrastructure and human lives. Early prediction framework of such disaster can help to mitigate such damages. The present work deals with prediction framework for initiation of debris flow, which is developed and validated with real case study. In order to test reliability of prediction framework, back analysis of very recent landslide debris flow accrued in the study area, Taliye village of Konkan region of Maharashtra, India on 22 July 2021 was carried out. Simulation results of landslide stability were compared with the leaky barrel-based rainfall-water saturation algorithm. Relations of landslide stability with the water saturation were established through physically based approach using Geo-Studio analysis module. Leaky barrel algorithm was used for study location for monitoring effect of rainfall on water saturation. The result confirms the good predictability of landslide occurrence through a developed early prediction framework. The methodological framework was presented in this paper for prediction of shallow landslide occurrence and recommended for real-time monitoring of landslide prone locations.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

Similar content being viewed by others

Abbreviations

EWS:

Early warning system

LSEWS:

Landslide early warning system

SWCC:

Soil water characteristics curve

LEM:

Limit equilibrium model

VWC:

Volumetric water content

PVC:

Poly vinyl chloride

°C:

Degree Celsius

γd :

Dry density

G:

Specific gravity

γw :

Density of water

e:

Void ratio

W:

Percentage water content

Sr :

Degree of saturation

C:

Cohesion

Φ:

Angel of internal friction

θ:

Volumetric water content

I:

Rainfall intensity

Z:

Rainfall stored in barrel

Zc :

Capacity of system

Δ:

Drainage rate

Kd :

Drainage Coefficient

T:

Time

References

  1. Froude M J and Petley D N 2018 Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences 18(8): 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018

    Article  Google Scholar 

  2. Wicki A, Lehmann P, Hauck C, Seneviratne S I, Waldner P and Stähli M 2020 Assessing the potential of soil moisture measurements for regional landslide early warning. Landslides 17(8): 1881–1896. https://doi.org/10.1007/s10346-020-01400-y

    Article  Google Scholar 

  3. Sudani P and Patil K A 2023 Investigating effect of water saturation on stability of hill slope: Malin case study. Advances in Civil and Architectural Engineering. https://doi.org/10.13167/2023.26.9

    Article  Google Scholar 

  4. Sudani P and Patil K A 2023 Evaluating the significance of saturation in shallow landslide failure: saturated–unsaturated soil perspective. Multiscale and Multidisciplinary Modeling, Experiments and Design.. https://doi.org/10.1007/s41939-023-00175-8

    Article  Google Scholar 

  5. Sudani P and Patil K A 2023 Assessment of variation in degree of saturation due to rainfall for landslide study. Geotechnical Engineering Journal 54(2): 36–40

    Google Scholar 

  6. Sudani P, Patil K A and Kolekar Y A 2023 Historical development of landslide early warning system (LEWS): a review. Lecture Notes in Civil Engineering. https://doi.org/10.1007/978-981-19-7245-4_24

    Article  Google Scholar 

  7. Michoud C, Bazin S, Blikra L H, Derron M-H and Jaboyedoff M 2013 Experiences from site-specific landslide early warning systems. Natural Hazards and Earth System Sciences 13(10): 2659–2673. https://doi.org/10.5194/nhess-13-2659-2013

    Article  Google Scholar 

  8. Guzzetti F, Gariano S L, Peruccacci S, Brunetti M T, Marchesini I, Rossi M and Melillo M 2020 Geographical landslide early warning systems. Earth-Science Reviews 200: 102973. https://doi.org/10.1016/j.earscirev.2019.102973

    Article  Google Scholar 

  9. Bogaard T A and andGreco R, 2016 Landslide hydrology: from hydrology to pore pressure. WIREs Water 3(3): 439–459. https://doi.org/10.1002/wat2.1126

    Article  Google Scholar 

  10. Harilal G T, Madhu D, Ramesh M V and Pullarkatt D 2019 Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India. Landslides 16(12): 2395–2408. https://doi.org/10.1007/s10346-019-01244-1

    Article  Google Scholar 

  11. Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M and Galli M 2006 Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2): 166–184. https://doi.org/10.1016/j.geomorph.2006.04.007

    Article  Google Scholar 

  12. Aleotti P 2004 A warning system for rainfall-induced shallow failures. Engineering Geology 73(3–4): 247–265. https://doi.org/10.1016/j.enggeo.2004.01.007

    Article  Google Scholar 

  13. Lagomarsino D, Segoni S, Fanti R and Catani F 2013 Updating and tuning a regional-scale landslide early warning system. Landslides 10(1): 91–97. https://doi.org/10.1007/s10346-012-0376-y

    Article  Google Scholar 

  14. Badoux A, Graf C, Rhyner J, Kuntner R and McArdell B W 2009 A debris-flow alarm system for the Alpine Illgraben catchment: design and performance. Natural Hazards 49(3): 517–539. https://doi.org/10.1007/s11069-008-9303-x

    Article  Google Scholar 

  15. Baum R L and Godt J W 2010 Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides 7(3): 259–272. https://doi.org/10.1007/s10346-009-0177-0

    Article  Google Scholar 

  16. Bai S, Wang J, Thiebes B, Cheng C and Yang Y 2014 Analysis of the relationship of landslide occurrence with rainfall: a case study of Wudu County. China. Arabian Journal of Geosciences 7(4): 1277–1285. https://doi.org/10.1007/s12517-013-0939-9

    Article  Google Scholar 

  17. Sudani P and Patil K A 2022 Evolution of early warning system for landslides. Disaster Advances 15(8): 46–59. https://doi.org/10.25303/1508da046059

    Article  Google Scholar 

  18. Salciarini D, Brocca L, Camici S, Ciabatta L, Volpe E, Massini R and Tamagnini C 2019 Physically based approach for rainfall-induced landslide projections in a changing climate. Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 172(6): 481–495. https://doi.org/10.1680/jgeen.18.00216

    Article  Google Scholar 

  19. Wei Y, Wu X, Xia J, Miller G A, Cai C, Guo Z and Arash H 2019 The effect of water content on the shear strength characteristics of granitic soils in South China. Soil and Tillage Research 187: 50–59. https://doi.org/10.1016/j.still.2018.11.013

    Article  Google Scholar 

  20. Mondini A, Carlà R, Reichenbach P, Cardinali M and Guzzetti F 2009. Use of a remote sensing approach to detect landslide thermal behaviour. In: EGU General Assembly Conference. 11, 7790

  21. Abraham M T, Satyam N, Pradhan B, Segoni S and Alamri A 2022 Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring. Geosciences Journal 26(2): 289–301. https://doi.org/10.1007/s12303-021-0026-2

    Article  Google Scholar 

  22. Fiorillo F and Wilson R C 2004 Rainfall induced debris flows in pyroclastic deposits, Campania (southern Italy). Engineering Geology 75(3–4): 263–289. https://doi.org/10.1016/j.enggeo.2004.06.014

    Article  Google Scholar 

  23. Keefer D K, Wilson R C, Mari R K, Brabb E E, Iii W M B, Ellen S D, Harp E L, Wieczorek G F, Alger C S and Zatkint R S 1987 Real-time landslide warning during heavy rainfall. Science 238(13): 921–925

    Article  Google Scholar 

  24. Petley D 2021 The disastrous Taliye village landslide in Maharashtra. Landslide blog, India

    Google Scholar 

  25. Stark T D, Choi H and McCone S 2005 Drained shear strength parameters for analysis of landslides. J. Geotech. Geoenvironmental Eng. 131(5): 575–588. https://doi.org/10.1061/(asce)1090-0241(2005)131:5(575)

    Article  Google Scholar 

  26. Watabe Y, Tanaka M and Kikuchi Y 2009 Practical determination method for soil parameters adopted in the new performance based design code for port facilities in Japan. Soils Found. 49(6): 827–839

    Article  Google Scholar 

  27. Ali F, Farooq K, Mujtaba H, Riaz A and Ulhaq E 2016 Influence of saturation on rainfall generated landslides in shale along Murree-Kohala road. Pakistan J. Geol. Soc. India 88(6): 718–724. https://doi.org/10.1007/s12594-016-0539-x

    Article  Google Scholar 

  28. Morgenstern N U and Price V E 1965 The analysis of the stability of general slip surfaces. Geotechnique 15(1): 79–93

    Article  Google Scholar 

  29. Hiura H and Sassa K 1985. Estimation of the landslide drainage effect in use of tank model. In: A. Takei (Ed.), Procedings of international symposium on errosion, debris flow and disaster prevention pp. 355–360

  30. Tan K S, Lim S B, Tan T L and Yang S L 1987. Landslide problems and their control in Singapore. In: Southeast Asian Geotechnical Conference., 25–36

  31. Wilson R C and Wieczorek G F 1995 Rainfall thresholds for the initiation of debris flows at La Honda, California. Environmental & Engineering Geoscience 1(1): 11–27. https://doi.org/10.2113/gseegeosci.i.1.11

    Article  Google Scholar 

Download references

Acknowledgements

We express our gratitude to the AICTE-NDF Scheme (Application number 60400) for awarding us a research fellowship, which has enabled us to pursue our research endeavors. Additionally, we extend our appreciation to the College of Engineering Pune, which has facilitated our academic pursuits.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, Pune, Maharashtra, 411005, India

    Prashant Sudani & K A Patil

Authors
  1. Prashant Sudani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K A Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prashant Sudani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sudani, P., Patil, K.A. Early prediction framework for a rainfall-induced landslide: validation through a real case study. Sādhanā 48, 187 (2023). https://doi.org/10.1007/s12046-023-02242-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-023-02242-9

Keywords

Search

Navigation