scholarly journals Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability

Landslides ◽  
2021 ◽  
Author(s):  
David J. Peres ◽  
Antonino Cancelliere
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Statistical Properties ◽  
Data Availability ◽  
Practical Advantage ◽  
Rainfall Events ◽  
Landslide Early Warning ◽  
Sampling Variation ◽  
Triggering Events ◽  
Triggering Rainfall

AbstractRainfall intensity-duration landslide-triggering thresholds have become widespread for the development of landslide early warning systems. Thresholds can be in principle determined using rainfall event datasets of three types: (a) rainfall events associated with landslides (triggering rainfall) only, (b) rainfall events not associated with landslides (non-triggering rainfall) only, (c) both triggering and non-triggering rainfall. In this paper, through Monte Carlo simulation, we compare these three possible approaches based on the following statistical properties: robustness, sampling variation, and performance. It is found that methods based only on triggering rainfall can be the worst with respect to those three investigated properties. Methods based on both triggering and non-triggering rainfall perform the best, as they could be built to provide the best trade-off between correct and wrong predictions; they are also robust, but still require a quite large sample to sufficiently limit the sampling variation of the threshold parameters. On the other side, methods based on non-triggering rainfall only, which are mostly overlooked in the literature, imply good robustness and low sampling variation, and performances that can often be acceptable and better than thresholds derived from only triggering events. To use solely triggering rainfall—which is the most common practice in the literature—yields to thresholds with the worse statistical properties, except when there is a clear separation between triggering and non-triggering events. Based on these results, it can be stated that methods based only on non-triggering rainfall deserve wider attention. Methods for threshold identification based on only non-triggering rainfall may have the practical advantage that can be in principle used where limited information on landslide occurrence is available (newly instrumented areas). The fact that relatively large samples (about 200 landslides events) are needed for a sufficiently precise estimation of threshold parameters when using triggering rainfall suggests that threshold determination in future applications may start from identifying thresholds from non-triggering events only, and then move to methods considering also the triggering events as landslide information starts to become more available.

Which method should we use to draw empirical rainfall thresholds for landslide early warning?

2020 ◽  
Author(s):  
David Johnny Peres ◽  
Antonino Cancelliere
Keyword(s):  
Early Warning ◽  
Confusion Matrix ◽  
Early Warning Systems ◽  
Statistical Properties ◽  
Posteriori Probability ◽  
Lower Envelope ◽  
Reliable Determination ◽  
A Posteriori Probability ◽  
Triggering Events ◽  
Landslide Data

<p>Landslide thresholds determined empirically through the combined analysis of rainfall and landslide data are at the core of early warning systems. Given a set of rainfall and landslide data, several methods do exist to determine the threshold: methods based on triggering events only, methods based on the non-triggering events only, and methods based on both type of rainfall events. The first are the most commonly encountered in literature. Early work determined the threshold by drawing the lower envelope curve of the triggering events “by eye”. More recent work used more sophisticated statistical approaches in order to reduce the subjectivity. Among these methods, the so-called frequentist method has become prominent in the literature. These methods have been criticized because they do not account uncertainty, i.e. the fact that there is not a clear separation between rainfall characteristics of triggering and non-triggering events. Hence, methods based on the optimization of Receiver operating characteristic indices – count of true and false positives/negatives – have been proposed. One of the first methods proposed in this sense referred to the use of Bayesian a-posteriori probability, which is the same of using the so-called ROC Precision index. Others have used the True Skill Statistic. On the other hand, use of non-triggering events only has been discussed just by a few researchers, and the potentialities of this way to proceed have been scarcely explored.</p><p>The choice of the method is usually dictated by external factors, such as the availability of data and their reliability, but it should also take into account of the theoretical statistical properties of each method.</p><p>Given this context, in the present work we compare, through Monte Carlo simulations, the statistical properties of each of the above-mentioned methods. In particular, we attempt to provide the answer to the following questions: What is the minimum number of landslides that is needed to perform a reliable determination of thresholds? How robust is the method for drawing the threshold – i.e. their sensitivity to artifacts in the data, such as exchanges of triggering events with non-triggering events due to incompleteness of landslide archives? What are the performances of the methods in terms of the whole ROC confusion matrix?</p><p>The analysis is performed for various levels of uncertainty in the data, i.e. noise in the separation by triggering and non-triggering events. Results show that methods based on non-triggering events only may be convenient when few landslide data are available. Also, in the case of high uncertainty in the data, the performances of methods based on triggering events may be poor compared to those based on non-triggering events. Finally, the methods based on both triggering and non-triggering events are the most robust.</p>

scholarly journals Brief communication "Landslide Early Warning System: toolbox and general concepts"

2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
End Users ◽  
Flow Chart ◽  
Warning Systems ◽  
Limited Experience ◽  
Practical Guidelines ◽  
Landslide Early Warning

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.

Five years of EDuMaP for the performance analysis of territorial landslide early warning systems

2021 ◽  
Author(s):  
Luca Piciullo ◽  
Michele Calvello
Keyword(s):  
Performance Evaluation ◽  
Performance Analysis ◽  
Performance Assessment ◽  
Early Warning ◽  
Early Warning Systems ◽  
Performance Criteria ◽  
Warning Systems ◽  
Landslide Event ◽  
Landslide Early Warning ◽  
Warning Model

<p>Landslide early warning systems (LEWS) can be classified in either territorial or local systems (Piciullo et al., 2018). Systems addressing single landslides, at slope scale, can be named local LEWS (Lo-LEWS), systems operating over wide areas, at regional scale, can be referred to as territorial systems (Te-LEWS). Te-LEWS deal with the occurrence of several landslides within wide warning zones at municipal/regional/national scale. Nowadays, there are around 30 Te-LEWS operational worldwide (Piciullo et al., 2018; Guzzetti et al., 2020). The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. Often the performance evaluation is based on 2 by 2 contingency tables computed for the joint frequency distribution of landslides and alerts, both considered as dichotomous variables. This approach can lead to an imprecise assessment of the warning model, because it cannot differentiate among different levels of warning and the variable number of landslides that may occur in a time interval.</p><p>To overcome this issue Calvello and Piciullo (2016) proposed an original method for the performance analysis of a warning model, named EDuMaP, acronym of the method’s three main phases: Event analysis, Duration Matrix computation, Performance assessment. The method is centered around the computation of a n by m duration matrix that quantifies the time associated with the occurrence (and non-occurrence) of a given landslide event in relation to the different warning levels adopted by a Te-LEWS. Different performance criteria and indicators can be applied to evaluate the computed duration matrix.</p><p>Since 2016, the EDuMaP method has been applied to evaluate the performance of several Te-LEWS operational worldwide: Rio de Janeiro, Brazil (Calvello and Piciullo, 2016); Norway, Vestlandet (Piciullo et al., 2017a); Piemonte region, Italy (Piciullo et al., 2020), Amalfi coast, Italy (Piciullo et al., 2017b). These systems have different structures and warning models with either fixed or variable warning zones. In all cases, the EDuMaP method has proved to be flexible enough to successfully perform the evaluation of the warning models, highlighting critical and positive aspects of such systems, as well as proving that simpler evaluation methods do not allow a detailed assessment of the seriousness of the errors and of the correctness of the predictions of Te-LEWS (Piciullo et al., 2020).</p><p>Calvello M, Piciullo L (2016) Assessing the performance of regional landslide early warning models: the EDuMaP method. Nat Hazards Earth Syst Sc 16:103–122. https://doi.org/10.5194/nhess-16-103-2016</p><p>Guzzetti et al (2020) Geographical landslide early warning systems. Earth Sci Rev 200:102973. https://doi.org/10.1016/j.earsc irev.2019.102973</p><p>Piciullo et al (2018) Territorial early warning systems for rainfall-induced landslides. Earth Sci Rev 179:228–247. https://doi.org/10.1016/j.earscirev.2018.02.013</p><p>Piciullo et al (2017a) Adaptation of the EDuMaP method for the performance evaluation of the alerts issued on variable warning zones. Nat Hazards Earth Sys Sc 17:817–831. https://doi.org/10.5194/nhess-17-817-2017</p><p>Piciullo et al (2017b) Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides. Landslides 14:995–1008. https://doi.org/10.1007/s10346-016-0750-2</p><p>Piciullo et al (2020). Standards for the performance assessment of territorial landslide early warning systems. Landslides 17:2533–2546. https://doi.org/10.1007/s10346-020-01486-4</p>

Enhancing the reliability of landslide early warning systems by machine learning

Landslides ◽  
2020 ◽  
Vol 17 (9) ◽  
pp. 2231-2246
Author(s):  
Hemalatha Thirugnanam ◽  
Maneesha Vinodini Ramesh ◽  
Venkat P. Rangan
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Landslide Early Warning

Development of early warning system for linear engineering - a case study on highway in Sichuan, China

2020 ◽  
Author(s):  
Ruihua Xiao
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Highway Safety ◽  
Warning Systems ◽  
Safety Control ◽  
Early Warning Model ◽  
Landslide Early Warning ◽  
Warning Model

<p>For the recent years, highway safety control under extreme natural hazards in China has been facing critical challenges because of the latest extreme climates. Highway is a typical linear project, and neither the traditional single landslide monitoring and early warning model entirely dependent on displacement data, nor the regional meteorological early warning model entirely dependent on rainfall intensity and duration are suitable for it. In order to develop an efficient early warning system for highway safety, the authors have developed an early warning method based on both monitoring data obtained by GNSS and Crack meter, and meteorological data obtained by Radar. This early-warning system is not each of the local landslide early warning systems (Lo-LEWSs) or the territorial landslide early warning systems (Te-LEWSs), but a new system combining both of them. In this system, the minimum warning element is defined as the slope unit which can connect a single slope to the regional ones. By mapping the regional meteorological warning results to each of the slope units, and extending the warning results of the single landslides to the similar slope units, we can realize the organic combination of the two warning methods. It is hopeful to improve the hazard prevention and safety control for highway facilities during critical natural hazards with the progress of this study.</p>

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

scholarly journals Simulated or measured soil moisture: Which one is adding more value to regional landslide early warning?

2021 ◽  
Author(s):  
Adrian Wicki ◽  
Per-Erik Jansson ◽  
Peter Lehmann ◽  
Christian Hauck ◽  
Manfred Stähli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Prediction Models ◽  
Numerical Models ◽  
Forecast Model ◽  
Early Warning Systems ◽  
Transfer Model ◽  
Statistical Framework ◽  
Landslide Early Warning

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWS). However, it is still unclear which source of information – numerical models or in-situ measurements – are of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019 using a physically-based 1D soil moisture transfer model (CoupModel). A common parametrization set was defined for all sites except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in-situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and ROC analysis was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different CoupModel parametrizations, to various distances between simulation sites and landslides, and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance-dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

scholarly journals Landslide early warning based on failure forecast models: the example of Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 3 (2) ◽  
pp. 1511-1525 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Warning Systems ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Critical Phases ◽  
Temporal Windows

Abstract. We investigate the use of landslide failure forecast models by exploiting near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here we describe the main concepts of our method, and show an example of application to a real emergency scenario, the La Saxe rockslide, Aosta Valley region, northern Italy. Based on the herein presented case study, we identify operational thresholds based on the reliability of the forecast models, in order to support the management of early warning systems in the most critical phases of the landslide emergency.

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