Landslide Mitigation Informatics (LIMIT): Effective decision-making for complex landslide geohazards.

Lead Research Organisation: Northumbria University
Department Name: Fac of Engineering and Environment

Abstract

Landslides or the threat of landslides can cause significant economic disruption and pose a risk to life. Relatively small events can affect wide areas, particularly where the primary road network is sparse and there is limited scope for rerouting and diversion. Rainfall triggers the majority of landslides in the U.K. and national level 24-hr forecasts exist (for emergency response agencies), but there is uncertainty surrounding what combination(s) of duration and intensity trigger slope failures on a site specific level and why similar events do not always lead to the same event/no-event outcome. These knowledge gaps are critical where decisions must actively be made to warn users of (or close) linear infrastructure such as roads and rail in order to saves lives and costs. This lack of specificity, combined with the high costs of traditionally instrumenting known 'at risk' locations, hinders effective decision-making for key authorities and their partners. As a result many essential components of the environment are not monitored in advance, or on a wide-scale / high-resolution (spatial and temporal) basis. LIMIT will make use of and develop the next generation of low-cost and low-power integrated network (and networks of networks) sensors combined with edge processing and multi-threshold trigger based streaming of key data in near real-time to allow decisions underpinned by advanced theories of failure mechanics. The result is low cost, wide coverage provision of data that analyses the state of the environment and forecasts future behaviour at higher spatial and temporal resolutions than previously possible, integrated into a seamless 'data chain' from site to decision-makers. Data and key derivations based on fundamental process science are automatically ingested/shared into a newly constructed digital environment via an intelligent hierarchical platform. The outputs are fit for national data sets and modelling; policy makers deciding on sensor networks for monitoring evolving risk due to long-term environmental changes; operational decision-makers tasked with real-time management of acute threats to life; right though to data provision and two-way engagement with the individuals at risk. Innovative low-cost, in situ near real-time data streaming/processing sensors resiliently linked to an integrated portal with automated reporting offers a viable and transformative solution to end-user challenges.

The LIMIT feasibility study will generate new field validated intelligent monitoring informatics, underpinned by advanced theories of failure mechanics, to provide critical data on the increasing likelihood and then the occurrence of slope failures in real-time.

Planned Impact

WP3 output white paper will be used by the Scottish Government and Transport Scotland (TS), and their operating companies / contractors to review the acute response and long-term monitoring strategy for hazardous slopes effecting trunk-road infrastructure in Scotland. The Geotechnical Manager for TS is the primary user, who is responsible for advising TS / Scottish Government and directing the operating company's landslide response. Improved planning and decision making will be evidence-based via a business case and the outputs of WP1-WP3 on the required investment in monitoring partnered to infrastructure works to deliver processed data in real and near real-time.

WP1-WP3 will be used throughout the 12 month project by TS and their operating companies to respond to detected landslides, and the increased threat of landslides using our new and integrated sensors and processing. This will be used in the deployment of additional monitoring / staff resources.

Short-term impact will be measured and evidenced by appropriate responses of both TS and the operating company to detected events or quiescence in assumed unstable areas using the newly constructed digital environment. We anticipate a dramatic reduction in false alarms (warnings activated but no landslides occurring) and far more proactive investigations of areas of concern as we implement our monitoring approach, responsively document events, and refine the protocols for follow up monitoring after an 'event'.

Medium-term to long-term impact and Partner benefits can be measured with the acceptance of monitoring recommendations as part of TS policy, and, follow-on monitoring contracts and implementations to third parties, the operating companies, or Universities. In addition journey delays and their economic impacts can be evaluated against former best practice decision making chains.

Although we do not directly engage with the final end-users, the general public, at this feasibility and validation stage they will be directly impacted through decreased risk. In the short-term increased knowledge of areas of concern may actually trigger more road closure days and use of the diversion route, but, this potentially negative impact has to be viewed against the overall lowering of individual risk and long-term improvements in the reliability and effectiveness of mitigation measures.
 
Description Despite the impacts of COVID on funding and travel we have generated significant and notable success in this project. We have develop a new automatic change detection and quantification tool using low-cost time-lapse imagery. This approach took open source code developed for controlled laboratory tests and added significant innovations and sophistication to enable it to be applies in challenging natural environments (https://doi.org/10.3390/rs13050893).

The new time-lapse automatic detection system has been successfully applied to provide vital early warning at perhaps the most challenging site for landslide hazards in Scotland, the Rest and Be Thankful on the A83. We were able to provide early warning to help authorities avoid imminent threats to road users and operational staff and have been in continuous contact with the operator providing updates on slope movements during subsequent extreme rainfall events. The code has been so successful and has considerable potential for other applications and so we have now made it open source and publicly available (https://github.com/Khan1988/PIVlab-for-landslide-monitoring).

We have also installed and streamed seismic instruments that have helped us detect, locate and analyse landslides events. This advance is significant because it complements the time-lapse imagery analysis system which is ineffective in low visibility conditions. We have further developed low-cost versions of these off-the-shelf devices in order to provide more versatile, sophisticated and extended coverage of the seismic monitoring. These are in the calibration phase due to COVID impacts.

We have also developed sophisticated processing of rainfall data in combination with the above innovations to better understand and forecast the link between landslides and particular rainfall events. Accounting for antecedent conditions enables to refine the thresholds used to alert authorities of potentially hazardous conditions (https://doi.org/10.31223/X52W2R).
Exploitation Route We have developed a new open source code which can be used on any time-lapse imagery of changing phenomena. Interest has already been raised from the glaciological community for tracking glacier movement for example.

We are currently testing our new low-cost, passive seismic sensors which could provide exciting new areas of monitoring advance, not just in terms of event detection but also in terms of analysing the preconditions (soil moisture levels) to failure.

We have developed a remote streaming system that draws in and analyses key data streams for decision-making - in collaboration with Newcastle University.
Sectors Digital/Communication/Information Technologies (including Software),Education,Environment,Transport

URL https://www.mdpi.com/2072-4292/13/5/893
 
Description Our new time-lapse system has been heavily utilised by Transport Scotland and its contractors during a series of damaging and costly events. Our on-site automatic detection enabled them to close the road or halt remediation works during imminent threats as the slope destabilised and likely saved lives. Also being able to see the deformations and understand what was happening enabled them to keep the road open as long as possible and plan where to hold back traffic safely. In 2020-21, as a result of debris flow activity the A83 was closed for approximately 120 days. It is estimated that the disruption caused by these events, on average, cost the authorities £90,000 daily so savings here are dramatic and impactful. The system was also presented to the Chartered Institution of Civil Engineering Surveyors and received wide spread attention and strong positive feedback, particularly from those affected by the A83 landslides or who work on it, and also led to new potential collaborations. Our data are also utilised by consultancy companies working on different aspects of the landslide problem such as the catch nets used for mitigation.
First Year Of Impact 2020
Sector Digital/Communication/Information Technologies (including Software),Environment,Transport
Impact Types Societal,Economic

 
Title Low-Cost Automatic Slope Monitoring Using Vector Tracking Analyses on Time-Lapse Imagery 
Description Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. Particle image velocimetry (PIV) algorithms are run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction. 
Type Of Material Data analysis technique 
Year Produced 2021 
Provided To Others? Yes  
Impact Only posted today! 
URL https://github.com/Khan1988/PIVlab-for-landslide-monitoring
 
Description Invited evening talk to the Chartered Institution of Civil Engineering Surveyors 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact The invited evening talk on this research project was delivered to the Chartered Institution of Civil Engineering Surveyors in December 2020 (by PI Lim) but remains available on their website to members. The talk itself was open to all and I'm told it was one of the best attended events and there were many interested comments and questions that led to a very enjoyable discussion about this challenging research area. There were several requests for follow on meetings, most notably with representatives from Network Rail.
Year(s) Of Engagement Activity 2020
URL https://www.cices.org/news/events/a-natural-landslide-laboratory-monitoring-slope-change-at-the-rest...