Identification of potential failure zones in large progressive landslides: a method to optimize the cost of landslide early warning

Saurabh Kumar, Soumya Darshan Panda, Sarada Prasad Pradhan () and Pallavi Chattopadhyay
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Saurabh Kumar: IIT Roorkee
Soumya Darshan Panda: IIT Roorkee
Sarada Prasad Pradhan: IIT Roorkee
Pallavi Chattopadhyay: IIT Roorkee

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 13, No 31, 12279-12311

Abstract: Abstract The cost of a landslide early warning system (LEWS) is a significant hurdle in its installation and adoption in developing nations like India, which has one of the highest rates of landslide occurrences worldwide. Previous research has focused on using low-cost monitoring instruments, cost-effective data transmission-management technologies, open-source technology, etc., to reduce the cost of LEWS. A possible strategy for reducing the price of a LEWS is to restrict the area that needs to be monitored. The cost of LEWS directly relates to the size of the slide. For instance, a massive landslide requires additional sensors to monitor the entire slide. However, for large landslides where progressive failure is confined to a particular zone, we must concentrate monitoring efforts on these critical areas for the optimal allocation of resources. For such slides, the number of sensors required can be reduced by focusing the deployment of sensors on the most vulnerable areas, leading to a lower cost. Hence, the current study proposes a generalized method for identifying potential failure zone/areas in large or deep-seated landslides that need to be monitored. This method not only provides economization of the number of sensors required but also ensures that data collection is focused and relevant, potentially enhancing the quality of monitoring and the accuracy of predictive models. The proposed method integrates different geotechnical approaches such as field investigation, laboratory testing, back analysis, and multi-temporal stability analysis. The method was tested on a deep-seated Kotropi landslide Himachal Pradesh initiated in 2017 and continuously experiencing progressive failures. A multi-temporal stability analysis was conducted in two phases. The first phase utilizes data collected during the 2018 field visit and estimates the probability of failure in different areas of the landslide. A field visit successfully validated the failure zone identified in the first phase. Furthermore, the second phase stability analysis, based on the data collected during the 2022 field visit, was performed to determine the future probability of failure in different slide areas. The in-depth analysis indicates that the Kotropi landslide is experiencing progressive failure, which limited to a particular zone in N and NW direction in contrast to the initial failure in NE-SW direction. Hence, using the proposed method, the area of a large landslide that needs to be monitored can be reduced by identifying the most vulnerable area, lowering the overall cost of a LEWS.

Keywords: Landslide; Landslide early warning system (LEWS); Back analysis; Probabilistic multi-temporal stability analysis; Electrical resistivity tomography (ERT) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06685-z

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