Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring

Abraham, MT; Satyam, N; Pradhan, B; Segoni, S; Alamri, A

Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring

Abraham, MT Satyam, N Pradhan, B

Segoni, S Alamri, A

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Geosciences Journal, 2022, 26, (2), pp. 289-301
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Abraham, MT
dc.contributor.author	Satyam, N
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Segoni, S
dc.contributor.author	Alamri, A
dc.date.accessioned	2023-03-20T03:39:31Z
dc.date.available	2023-03-20T03:39:31Z
dc.date.issued	2022-01-01
dc.identifier.citation	Geosciences Journal, 2022, 26, (2), pp. 289-301
dc.identifier.issn	1226-4806
dc.identifier.issn	1598-7477
dc.identifier.uri	http://hdl.handle.net/10453/167742
dc.description.abstract	Rainfall thresholds are commonly utilized to forecast landslides using the historical relationship between occurrence of slope failures and rainfall in an area. SIGMA (Sistema Integrato Gestione Monitoraggion Allerta) is a rainfall threshold model, which uses the statistical distribution of rainfall for forecasting the occurrence of landslides. The threshold curves are functions of standard deviation of the cumulated rainfall data, taking into account both long-term and short term-rainfall. To overcome the limitations of statistical rainfall threshold, the real-time monitoring data from MicroElectroMechanical Systems (MEMS) tilt sensors have been integrated with SIGMA model using a decisional algorithm for a test site (Kalimpong) in Darjeeling Himalayas, in the northeastern part of India. Three different models, the SIGMA model, tilt meter readings and the combination of both are compared quantitatively using the precipitation and landslide data of Kalimpong town between July 2017 and September 2020. The results indicate that the integration of tilt meter readings has lowered the number of false alarms issued by SIGMA model from 70 to 38 in the studied period, with an increase in the likelihood ratio from 18.10 to 20.23. The Receiver Operating Characteristic (ROC) curves indicate that the combined approach has the best performance among the models considered in this study, with an area under the curve 0.976. The proposed method was found to have better performance than the other rainfall thresholds derived for Kalimpong region so far, and the prototypal model can be further fine-tuned to develop an operational Landslide Early Warning System (LEWS) for the region.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Geosciences Journal
dc.relation.isbasedon	10.1007/s12303-021-0026-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	04 Earth Sciences
dc.subject.classification	Geology
dc.title	Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	04 Earth Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-20T03:39:29Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	2

Abstract:

Rainfall thresholds are commonly utilized to forecast landslides using the historical relationship between occurrence of slope failures and rainfall in an area. SIGMA (Sistema Integrato Gestione Monitoraggion Allerta) is a rainfall threshold model, which uses the statistical distribution of rainfall for forecasting the occurrence of landslides. The threshold curves are functions of standard deviation of the cumulated rainfall data, taking into account both long-term and short term-rainfall. To overcome the limitations of statistical rainfall threshold, the real-time monitoring data from MicroElectroMechanical Systems (MEMS) tilt sensors have been integrated with SIGMA model using a decisional algorithm for a test site (Kalimpong) in Darjeeling Himalayas, in the northeastern part of India. Three different models, the SIGMA model, tilt meter readings and the combination of both are compared quantitatively using the precipitation and landslide data of Kalimpong town between July 2017 and September 2020. The results indicate that the integration of tilt meter readings has lowered the number of false alarms issued by SIGMA model from 70 to 38 in the studied period, with an increase in the likelihood ratio from 18.10 to 20.23. The Receiver Operating Characteristic (ROC) curves indicate that the combined approach has the best performance among the models considered in this study, with an area under the curve 0.976. The proposed method was found to have better performance than the other rainfall thresholds derived for Kalimpong region so far, and the prototypal model can be further fine-tuned to develop an operational Landslide Early Warning System (LEWS) for the region.

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http://hdl.handle.net/10453/167742