scholarly journals Big data managing in a landslide Early Warning System: experience from a ground-based interferometric radar application

2017 ◽  
Author(s):  
Emanuele Intrieri ◽  
Federica Bardi ◽  
Riccardo Fanti ◽  
Giovanni Gigli ◽  
Francesco Fidolini ◽  
...  
Keyword(s):  
Big Data ◽  
Early Warning ◽  
Data Transfer ◽  
Risk Mitigation ◽  
Warning System ◽  
Early Warning Systems ◽  
Integrated System ◽  
Monitoring Systems ◽  
Landslide Monitoring ◽  
Landslide Risk

Abstract. A big challenge in terms or landslide risk mitigation is represented by the increasing of the resiliency of society exposed to the risk. Among the possible strategies to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as Critical Infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a Data Collecting And Processing Center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. In this paper we will focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC, and how issues such as big data transfer, real-time warning, line of sight correction and data validation in emergency conditions have been dealt with.

scholarly journals Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

2017 ◽  
Vol 17 (10) ◽  
pp. 1713-1723 ◽  
Author(s):  
Emanuele Intrieri ◽  
Federica Bardi ◽  
Riccardo Fanti ◽  
Giovanni Gigli ◽  
Francesco Fidolini ◽  
...  
Keyword(s):  
Big Data ◽  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Early Warning Systems ◽  
Integrated System ◽  
Monitoring Systems ◽  
Landslide Risk ◽  
Complex Data

Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

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 Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides

2017 ◽  
Author(s):  
Abhirup Dikshit ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Landslide Risk ◽  
Rainfall Forecasting ◽  
Landslide Occurrence ◽  
Mobility Function ◽  
Hourly Rainfall ◽  
Rainfall Model

Abstract. The development of an early warning system for landslides due to rainfall has become an indispensable part for landslide risk mitigation. This paper explains the application of the hydrological FLaIR (Forecasting of Landslides Induced by Rainfall) model, correlating rainfall amount and landslide events. The FLaIR model comprises of two modules: RL (Rainfall-Landslide) which correlates rainfall and landslide occurrence and RF (Rainfall-Forecasting) which allows simulation of future rainfall events. The model can predetermine landslides based on identification of mobility function Y(.) which links actual rainfall and incidence of landslide occurrence. The critical value of mobility function was analyzed using 1st July 2015 event and applying it to 2016 monsoon to validate the results. These rainfall thresholds presented can be improved with intense hourly rainfall and landslide inventory data. This paper describes the details of the model and its performance for the study area.

scholarly journals A decision support system (DSS) for critical landslides and rockfalls and its application to some cases in the Western Italian Alps

2017 ◽  
Author(s):  
Davide Bertolo
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Early Warning ◽  
Early Warning Systems ◽  
Decision Makers ◽  
Monitoring Systems ◽  
Landslide Monitoring ◽  
Civil Protection ◽  
Degree Of Confidence

Abstract. Operative geologists who are involved in emergency management have often to deal with the consequences of assuming critical and strongly impacting decisions in uncertain conditions. Geohazards induced by active landslides are one of the civil protection situations requiring such decisions. Nowadays, the monitoring of active landslides is almost always supported by numerical early warning systems, based on instrumental geotechnical and topographic networks. These networks provide numerical early warning thresholds, which are set up in order to activate alert conditions at various levels of criticality in an objective way. Despite these progresses the issue related to the possibility to dispatch false alerts has not yet effectively solved and that’s the reason why the critical stages of the decisional processes are frequently relying not only on quantitative thresholds but also on the subjective experience of the emergency managers. Therefore it is not so uncommon to read landslide-monitoring procedures that combine the quantitative information provided by the monitoring systems with the qualitative decisional elements coming from their professional experience in order to assume the most correct decision. It's therefore evident that such an approach weakens the objectiveness provided by instrumental monitoring systems but, at the same time, collecting geological empirical and qualitative data can strengthen an hypothesis like the one that an active landslide could finally collapse. Bayesian methods are frequently used in clinical decision making, another field of the human activity where critical decisions have to be made in a short time, combining objective values such as those provided by medical tests with diagnostic qualitative markers. Based on the methods of clinical diagnosis, the has author has elaborated a reliable and objective Bayesian Decision Support System (or DSS), developed to support the decision makers in assuming the most correct decisions based on all the elements, both quantitative and qualitative, that are available at a certain step of the decision process. Thanks to the Bayesian approach, the DSS allows also to assess the predictivity of any single decisional step, which is the probability that a monitored landslide actually collapses when particular diagnostic evidences are detected, either instrumental or observational. Hence the decision makers who are able to issue a civil protection alert when a given degree of confidence about the chance that a monitored landslide will collapse is reached. The degree of confidence associated to the civil protection alert can be declared in the alert bulletin (e.g.: 80 % or 93 %). The decisional process can be tracked and replied by everyone in complete transparency. It's therefore evident that such a DSS allows the civil protection authorities to increase the reliability of the alerts, reducing at the same time the so-called “cry wolf” effect and the discomfort related to evacuations and to other civil protection measures. As a matter of fact, the decisional process becomes clearer and the people’s trust in the civil protection systems is being strengthened by a more transparent emergency communication. The DSS here described is an evolution and a statistical improvement of the method adopted in 2013 and 2014 during the emergency of the Mont the la Saxe landslide, and is now being successfully applied to two other hazardous situations in the Aosta Valley Alps: the Brenva Site (Mont Blanc Massif) and the Berlachu site in the municipality of Lillianes (Lower Lys Valley).

Machine Learning: potential for local and regional deep-seated landslide nowcasting

2020 ◽  
Author(s):  
Adriaan van Natijne ◽  
Roderik Lindenbergh ◽  
Thom Bogaard
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Early Warning ◽  
Satellite Remote Sensing ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Warning Systems

<p>Where landslide hazard mitigation is impossible, Early Warning Systems are a valuable alternative to reduce landslide risk. To this extent nowcasting and Early Warning Systems for landslide hazard have been implemented mostly at local scale. Unfortunately, such systems are often difficult to implement at regional scale or in remote areas due to dependency on local sensors. However, in recent years various studies have demonstrated the effective application of Machine Learning for deformation forecasting of slow-moving, deep-seated landslides. Machine Learning, combined with satellite Remote Sensing products offers new opportunities for both local and regional monitoring of deep-seated landslides and associated processes.</p><p>Working from the key variables of the landslide process we selected the available satellite Remote Sensing products, the necessary assumptions for a satellite only application and evaluated the potential benefit of local information. In the absence of continuous, satellite deformation measurements, nowcasting of the system state will provide a short term deformation prediction. We demonstrate the opportunities of Machine Learning on multi-sensor monitored Austrian landslide and anticipate on the integration in an Early Warning System. Furthermore, we highlight the risks and opportunities arising from the limited physics constraints in Machine Learning.</p>

Definition of a new multi-level early warning procedure for landslide risk management

2020 ◽  
Author(s):  
Alessandro Valletta ◽  
Andrea Carri ◽  
Roberto Savi ◽  
Edoardo Cavalca ◽  
Andrea Segalini
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Risk Mitigation ◽  
Sampling Rate ◽  
Early Warning Systems ◽  
Coefficient Of Determination ◽  
Critical Event ◽  
Forecasting Model ◽  
Landslide Risk ◽  
False Alarms

<p>The identification of potentially critical events involving unstable slopes is a major aspect in the field of natural hazards risk mitigation and management. In this framework, Early Warning Systems (EWS) exploiting advanced technologies represent an efficient approach to decrease the risk generated by landslide phenomena, allowing to reduce the possibility of damages and losses of human lives. EWS effectiveness has increased significantly in recent years, thanks to relevant advances in sensing technologies and data processing. In particular, the introduction of innovative monitoring instrumentation featuring automatic procedures and increased performances in terms of sampling rate and accuracy has permitted to develop EWS characterised by a near-real time approach. Among the several aspects involved in the development of a reliable Early Warning System, one of the most important is the ability to minimize the dissemination of false alarms, which should be avoided or identified in advance. The approach proposed in this study represents a new procedure aimed to assess the hazard level posed by a potentially critical event, previously identified by analysing displacement monitoring data. The process is implemented in a near-real time EWS and defines a total of five different hazard levels, on the basis of the results provided by two different models, namely an accelerating trend identification criterion and a failure forecasting model based on the Inverse Velocity Method (IVM). In particular, the forecasting analysis is performed only if the dataset elaborated by the onset-of-acceleration model highlights a potentially critical behaviour, which represents a first alert level. Following levels are determined by different conditions imposed on three parameters featured by the failure forecasting model, i.e. dataset dimension, coefficient of determination R-squared, and number of sensors displaying an accelerating trend. As these criteria get fulfilled, it is assumed that the monitored phenomenon is gradually evolving towards a more critical condition, thus reaching an increasing alert level depending on the analysis results. According to this classification, it is possible to set up for each single threshold a dedicated warning message, which could be automatically issued to authorities responsible of monitoring activities, in order to provide an adequate dissemination of information concerning the ongoing event. Moreover, the proposed procedure allows to customize the alert approach, giving the possibility to issue warning messages only if a certain Level is reached during the analysis.</p>

Early Warning System Framework Proposal, Based on Big Data Environment

2019 ◽  
Vol 9 (1) ◽  
pp. 35-66
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic
Keyword(s):  
Big Data ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Data Environment ◽  
Set Up ◽  
Novel Concept ◽  
A Company

Early warning systems are made with the purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible. The Big Data environment gives new opportunities and new approaches in analytical processes. There are numerous ways how to set up early warning systems within a company. The Big Data environment forces companies to apply new ways of thinking and use new disposable data sources. This article gives a novel concept for an early warning system design within a company, which is applicable in different industries. The core of the proposed framework is a hybrid fuzzy expert system which can contain a variety of data mining predictive models responsible for some specific areas as addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within the rule blocks. As a part of this framework, SNA methods are also explained and introduced as powerful and unique tool to be used in modern early warning systems.

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 Communication architecture of an early warning system

2010 ◽  
Vol 10 (11) ◽  
pp. 2215-2228 ◽  
Author(s):  
M. Angermann ◽  
M. Guenther ◽  
K. Wendlandt
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Satellite Communication ◽  
Warning System ◽  
Early Warning Systems ◽  
Lessons Learned ◽  
Warning Systems ◽  
Communication Architecture ◽  
Design And Implementation ◽  
Communication Links

Abstract. This article discusses aspects of communication architecture for early warning systems (EWS) in general and gives details of the specific communication architecture of an early warning system against tsunamis. While its sensors are the "eyes and ears" of a warning system and enable the system to sense physical effects, its communication links and terminals are its "nerves and mouth" which transport measurements and estimates within the system and eventually warnings towards the affected population. Designing the communication architecture of an EWS against tsunamis is particularly challenging. Its sensors are typically very heterogeneous and spread several thousand kilometers apart. They are often located in remote areas and belong to different organizations. Similarly, the geographic spread of the potentially affected population is wide. Moreover, a failure to deliver a warning has fatal consequences. Yet, the communication infrastructure is likely to be affected by the disaster itself. Based on an analysis of the criticality, vulnerability and availability of communication means, we describe the design and implementation of a communication system that employs both terrestrial and satellite communication links. We believe that many of the issues we encountered during our work in the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) on the design and implementation communication architecture are also relevant for other types of warning systems. With this article, we intend to share our insights and lessons learned.

scholarly journals Quantifying the effectiveness of early warning systems for natural hazards

2016 ◽  
Vol 16 (1) ◽  
pp. 149-166 ◽  
Author(s):  
M. Sättele ◽  
M. Bründl ◽  
D. Straub
Keyword(s):  
Natural Hazards ◽  
Early Warning ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Mitigation Measures ◽  
Management Approach ◽  
Warning Systems ◽  
Integrated Risk ◽  
Framework Approach

Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

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