Abstract
A forecasting algorithm using Support Vector Regression (SVR) used to forecast potential landslides in Munnar region of Western Ghats, India (10.0892 N, 77.0597 E) is presented in this paper. Forecasting for the possibility of landslide is accomplished by forecasting the pore-water pressure (PWP) 24 h ahead of time, at different locations and across soil layers under the ground at varying depths, and computing Factor of Safety (FoS) of the slope. It is done by learning from the real-time sensor data gathered from Amrita University’s Wireless Sensor Network (WSN) system deployed in Western Ghats for monitoring and early warning of landslides. We use two variations of SVR, SVR-Historic and SVR-Adaptive. SVR-Historic algorithm is trained with the data from July 2011 to December 2015 and tested for the period from January to November 2016. SVR-Adaptive algorithm is adaptively trained from July-2011 onwards and tested for the period from January to November 2016. PWP and the computed FoS from both the algorithms are compared with the actual PWP and FoS data and the Mean Square Error (MSE) for the SVR-Historic model is found to be 48.726 and 0.002 whereas the MSE for SVR-Adaptive model is found to be 12.438 and 0.0007 respectively. The PWP and the computed FoS from both the algorithms are tested for correlation using Pearson’s correlation test, with 95% confidence interval and the coefficients for PWP is found to be 0.804 and 0.959 respectively with p-value of 2.2e−16, whereas for FoS it is 0.802 and 0.955 with p-value of 2.2e−16. The confidence intervals for PWP and FoS from both the models is 0.763 to 0.839 and 0.950 to 0.969 respectively. Among the two forecasting models, SVR-Adaptive model performs better with a low MSE of 12.438 and 0.0007 in forecasting PWP and the computed FoS values respectively and correlates with the real-time data ~95% of the times. Application of this forecasting algorithm in real-world can thus provide 24 h extra time for early warning which is a boon for government and public to prepare for landslides after early warnings.
References
Bozzano F, Cipriani I, Mazzanti P, Prestininzi A (2011) Displacement patterns of a landslide affected by human activities: insights from ground-based InSAR monitoring. Nat Hazards 59(3):1377–1396
Brocca L, Ponziani F, Melone F, Moramarco T, Berni N, Wagner W (2012) Improving landslide movement forecasting using ASCAT-derived soil moisture data. In: EGU general assembly conference abstracts, vol 14, p 2307
Caine N (1980) The rainfall intensity: duration control of shallow landslides and debris flows. Geografiska Ann Ser A Phys Geography, 23–27
Chae BG, Lee JH, Park HJ, Choi J (2015) A method for predicting the factor of safety of an infinite slope based on the depth ratio of the wetting front induced by rainfall infiltration. Nat Hazards Earth Sys Sci 15(8):1835–1849
Crozier MJ (1999) Prediction of rainfall-triggered landslides: A test of the antecedent water status model. Earth Surf Proc Land 24(9):825–833
Dore MH (2003) Forecasting the conditional probabilities of natural disasters in Canada as a guide for disaster preparedness. Nat Hazards 28(2–3):249–269
Dostál I, Putiška R, Kušnirák D (2014) Determination of shear surface of landslides using electrical resistivity tomography. Contrib Geophys Geodesy 44(2):133–147
Gabet EJ, Burbank DW, Putkonen JK, Pratt-Sitaula BA, Ojha T (2004) Rainfall thresholds for landsliding in the Himalayas of Nepal. Geomorphol 63(3):131–143
Herrera G, Fernández-Merodo JA, Mulas J, Pastor M, Luzi G, Monserrat O (2009) A landslide forecasting model using ground based SAR data: The Portalet case study. Eng Geol 105(3):220–230
Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910
Kuriakose SL, Sankar G, Muraleedharan C (2009) History of landslide susceptibility and a chorology of landslide-prone areas in the Western Ghats of Kerala. India Environ Geol 57(7):1553–1568
Loew S, Gschwind S, Gischig V, Keller-Signer A, Valenti G (2015). Monitoring and early warning of the 2012 Preonzo catastrophic rockslope failure. Landslides, 1–14
Lukose Kuriakose S, Sankar G, Muraleedharan C (2010) Landslide fatalities in the Western Ghats of Kerala, India. In: EGU general assembly conference abstracts, vol 12, p 8645
Ramesh MV (2014a) Design, development, and deployment of a wireless sensor network for detection of landslides. Ad Hoc Netw 13:2–18
Ramesh MV (2014) U.S. Patent No. 8,692,668. U.S. Patent and Trademark Office, Washington, DC
Ramesh MV, Vasudevan N (2012) The deployment of deep-earth sensor probes for landslide detection. Landslides 9(4):457–474
Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD (2003) Artificial intelligence: a modern approach, vol 2. Prentice hall, Upper Saddle River
Schmidt J, Turek G, Clark MP, Uddstrom M, Dymond JR (2008) Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions. Nat Hazards Earth Sys Sci 8(2):349–357
Segoni S, Battistini A, Rossi G, Rosi A, Lagomarsino D, Catani F, Moretti S, Casagli N (2015) Technical note: an operational landslide early warning system at regional scale based on space–time-variable rainfall thresholds. Nat Hazards Earth Sys Sci, 15(4):853–861
Soman KP, Loganathan R, Ajay V (2009) Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd, 477. ISBN:978-81-203-3435-9
Vijith H, Madhu G (2008) Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environ Geol 55(7):1397–1405
Acknowledgements
The authors would like to express their immense gratitude to Satguru Sri Mata Amritanandamayi Devi, the chancellor of Amrita University for her constant support and guidance in all the research activities. This work is partly funded by Ministry of Earth Sciences (MoES), Government of India under the project titled “Advancing Integrated Wireless Sensor Networks for Real-time monitoring and detection of Disasters” and partly funded by Amrita University. We wish to express our gratitude to Prof Balaji Hariharan and Ramesh Guntha for their valuable suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hemalatha, T., Ramesh, M.V., Rangan, V.P. (2017). Adaptive Learning Techniques for Landslide Forecasting and the Validation in a Real World Deployment . In: Mikoš, M., Vilímek, V., Yin, Y., Sassa, K. (eds) Advancing Culture of Living with Landslides. WLF 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-53483-1_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-53483-1_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53482-4
Online ISBN: 978-3-319-53483-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)