scholarly journals Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception

2018 ◽  
Vol 18 (12) ◽  
pp. 3179-3186 ◽  
Author(s):  
Samuele Segoni ◽  
Luca Piciullo ◽  
Stefano Luigi Gariano
Keyword(s):  
Performance Evaluation ◽  
Risk Perception ◽  
Early Warning ◽  
Early Warning Systems ◽  
Monitoring Systems ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

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>

scholarly journals Standards for the performance assessment of territorial landslide early warning systems

Landslides ◽  
2020 ◽  
Vol 17 (11) ◽  
pp. 2533-2546 ◽  
Author(s):  
Luca Piciullo ◽  
Davide Tiranti ◽  
Gaetano Pecoraro ◽  
Jose Mauricio Cepeda ◽  
Michele Calvello
Keyword(s):  
Performance Evaluation ◽  
Early Warning ◽  
Early Warning Systems ◽  
Variable Number ◽  
Time Interval ◽  
Warning Systems ◽  
Landslide Occurrence ◽  
Critical Issues ◽  
Landslide Early Warning ◽  
Warning Model

Abstract Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim of such systems is to forecast the increased probability of landslide occurrence in a given warning zone. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. This paper describes a new Excel user-friendly tool for the application of the EDuMaP method, originally proposed by (Calvello and Piciullo 2016). A description of indicators used for the performance evaluation of different Te-LEWS is provided, and the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the “SMART” warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need threshold refinement. A comparison of the performance of the SMART model with other models operating in different Te-LEWS has also been carried out, highlighting critical issues and positive aspects. Lastly, the SMART performance has been evaluated with both the EDuMaP and a standard 2 × 2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed assessment of the warning model, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval.

A new IoT geosensor network for cost-effective landslide early warning systems

2021 ◽  
Author(s):  
Moritz Gamperl ◽  
John Singer ◽  
Kurosch Thuro
Keyword(s):  
Early Warning ◽  
Low Cost ◽  
Cost Effective ◽  
Early Warning Systems ◽  
Sensor Nodes ◽  
Monitoring Systems ◽  
Warning Systems ◽  
Mems Sensors ◽  
Landslide Early Warning ◽  
Geosensor Network

<p>Worldwide, cities in mountainous areas struggle with increasing landslide risk as consequence of global warming and population growth, especially in low-income informal settlements. For these situations, current monitoring systems are often too expensive and too difficult to maintain. Therefore, innovative monitoring systems are needed in order to facilitate low-cost landslide early warning systems (LEWS) which can be applied easily.</p><p>Based on technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication standard, we are currently developing a cost-effective IoT (Internet of Things) geosensor network. It is specifically designed for local scale LEWS in informal settlements.</p><p>The system, which is open source and can be replicated without restrictions, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g. tilt sensor) on board and to which various additional sensors can be connected. The nodes are autonomous and can operate on standard batteries or solar panels. The sensor nodes can be installed on critical infrastructure such as house walls or foundations. Two of the possible additions are the Subsurface Sensor Node and the Low-Cost Inclinometer. Both are installed underground and offer tilt- and groundwater-measurements of the subsurface.</p><p>Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed monitoring system offers a great cost to benefit ratio and easy application for similar sites and LEWS, especially in urbanized areas in developing countries.</p><p>This work is being developed as part of the project Inform@Risk, where the monitoring system will be installed as part of an early warning system in Medellín, Colombia. It is funded by the German Ministry of Education and Research (BMBF).</p>

scholarly journals Experiences from site-specific landslide early warning systems

2013 ◽  
Vol 13 (10) ◽  
pp. 2659-2673 ◽  
Author(s):  
C. Michoud ◽  
S. Bazin ◽  
L. H. Blikra ◽  
M.-H. Derron ◽  
M. Jaboyedoff
Keyword(s):  
Early Warning ◽  
Conceptual Models ◽  
Early Warning Systems ◽  
Monitoring Systems ◽  
Special Focus ◽  
Warning Systems ◽  
Threshold Values ◽  
Landslide Early Warning ◽  
Alarm Threshold ◽  
Set Up

Abstract. Landslide early warning systems (EWSs) have to be implemented in areas with large risk for populations or infrastructures when classical structural remediation measures cannot be set up. This paper aims to gather experiences of existing landslide EWSs, with a special focus on practical requirements (e.g., alarm threshold values have to take into account the smallest detectable signal levels of deployed sensors before being established) and specific issues when dealing with system implementations. Within the framework of the SafeLand European project, a questionnaire was sent to about one-hundred institutions in charge of landslide management. Finally, we interpreted answers from experts belonging to 14 operational units related to 23 monitored landslides. Although no standard requirements exist for designing and operating EWSs, this review highlights some key elements, such as the importance of pre-investigation work, the redundancy and robustness of monitoring systems, the establishment of different scenarios adapted to gradual increasing of alert levels, and the necessity of confidence and trust between local populations and scientists. Moreover, it also confirms the need to improve our capabilities for failure forecasting, monitoring techniques and integration of water processes into landslide conceptual models.

Comparison of the performance of different Territorial Landslide Early Warning Systems

2020 ◽  
Author(s):  
Jose Cepeda ◽  
Piciullo Luca ◽  
Tirante Davide ◽  
Pecoraro Gaetano ◽  
Calvello Michele
Keyword(s):  
Performance Evaluation ◽  
Early Warning ◽  
Climate Adaptation ◽  
Early Warning Systems ◽  
Variable Number ◽  
Original Method ◽  
Time Interval ◽  
Warning Systems ◽  
Landslide Early Warning ◽  
Warning Model

<p>Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim for such systems is to forecast the increased probability of landslides occurrence in a given warning zone. Nowadays, there are around 30 Te-LEWS operational worldwide. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. Often, a contingency matrix 2x2, usually employed for rainfall thresholds validation purposes, is used. Recently an original method has been proposed by Calvello and Piciullo, 2016: the EDuMaP.</p><p>This paper describes the new excel user-friendly tool for the application of the method. Moreover, a description of different indicators used for the performance evaluation of different Te-LEWS is provided. Subsequently, the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the SMART warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need thresholds refinement. The SMART performance has been evaluated with both the EDuMaP and a 2x2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed analysis, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval. Moreover, a comparison of the performance of different Te-LEWS with the SMART model has been carried out highlighting critical issues and positive aspects. Finally, the weakness aspects and the future developments of the SMART warning model are described.</p><p>This paper has been conceived in the context of the research-based innovation project Klima 2050 - "Risk reduction through climate adaptation of buildings and infrastructure" http://www.klima2050.no/.</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.

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

scholarly journals Regional rainfall thresholds for landslide occurrence using a centenary database

2017 ◽  
Author(s):  
Teresa Vaz ◽  
José Luís Zêzere ◽  
Susana Pereira ◽  
Sérgio C. Oliveira ◽  
Ricardo A. C. Garcia ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Period Analysis ◽  
Landslide Occurrence ◽  
Landslide Event ◽  
Landslide Early Warning ◽  
Regional Rainfall

Abstract. This work proposes a comprehensive methodology to assess rainfall thresholds for landslide initiation, using a centenary landslide database associated with a single centenary daily rainfall dataset. The methodology is applied to the Lisbon region and include the rainfall return period analysis that was used to identify the critical rainfall combination (quantity-duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds is assessed and validated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, such thresholds may be used with acceptable confidence up to 50 km distance from the rain gauge. The obtained rainfall thresholds using linear and potential regression have a good performance in ROC metrics. However, the intermediate thresholds based on the probability of landslide events, established in the zone between the lower limit threshold and the upper limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

Sign in / Sign up

Export Citation Format

Share Document