Improving the reliability of the decision-making process in a rockslide Early Warning procedure

2020 ◽  
Author(s):  
Davide Bertolo
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Early Warning Systems ◽  
Direct Perception ◽  
Economic Losses ◽  
Decision Making Process ◽  
Civil Protection ◽  
Integrated Monitoring ◽  
Italian Economy ◽  
Protection Measures

<p>During the last decades, the progresses in rock slope monitoring improved the reliability of the Early Warning Systems (EWS) all over the world. Among their features, EWS are designed to provide to the decision makers objective tools in order to support their decisions in activating emergency plans.</p><p>The choice to design an EW System, if only based on displacement or rainfall thresholds, may not be sufficient to support the decision-making process, when the monitored rockslide is threatening high value targets, both in terms of exposed human lives and potential economic losses.</p><p>As a matter of fact, the integrated monitoring systems usually installed on active rock slopes provide many different data about the behaviour of these phenomena. Most of these data are worth to be weighted in the decisional process, as they are relevant to confirm a specific event scenario.</p><p>In addition, experts and EWS managers are facing an increasing demand by the stakeholders and the population, to effectively communicate in a user-friendly way the decision-making process, as well as the uncertainty degree associated with each decisional step.</p><p>That is a necessity which becomes critical in the moment when the population and the stakeholders have no direct perception of a potential catastrophic event and the civil protection measures are preventively activated before the emergency.</p><p>The aim of this work is to present the Early Warning procedure elaborated by the regional Geological survey of the Aosta Valley Autonomous Region (Italy), which is based on the experience derived from the emergency management of the Mont de La Saxe rockslide in 2013. </p><p>The new EW procedure has been successfully tested for the first time during the rockslide activation in spring 2014 and it has been refined and improved during the following years.</p><p>The potential collapse of the Mont de La Saxe rockslide threatens a part of the important touristic resort of Courmayeur and the E25 Motorway, one the most important national communication axes, connecting the industrial areas of the Northern Italy with France and Switzerland.</p><p>In such a sensitive situation, a not sufficiently motivated alert could have led to impacting civil protection measures like the evacuation of two villages and the traffic interruptions, damaging the Italian economy and the regional tourism.</p><p>Therefore, the EW managers have decided to strengthen the existing EW procedure, based on displacement thresholds, in order to achieve the maximum amount of confidence in the decisional process. The new procedure is based on a Bayesian inferential process, combining the available data provided by the monitoring system.</p><p>Thanks to this approach a quantitative degree of confidence can be assigned to each decisional step, increasing the warning levels up to the declaration of the emergency condition.</p><p>At the same time, the new EW procedure provides a transparent and replicable decisional process, where the confidence degree associated to the civil protection alert can be declared in the alert bulletins.</p>

scholarly journals The Role of Seismological Networks in Civil Protection in Low and High Seismic Hazards

2021 ◽  
Author(s):  
Erzsébet Győri ◽  
Arman Bulatovich Kussainov ◽  
Gyöngyvér Szanyi ◽  
Zoltán Gráczer ◽  
Kendebay Zhanabilovich Raimbekov ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Civil Protection ◽  
Warning Systems ◽  
Ground Shaking ◽  
Research Areas ◽  
Seismic Networks ◽  
Strong Ground

Earthquakes are one of the most devastating natural disasters on Earth, causing sometimes huge economic losses and many human casualties. Since earthquake prediction is not yet possible, the purpose of civil protection is to reduce damage and protect human lives, in which the seismological networks of different countries play a very important role. Special applications of seismic networks are the early warning systems that can be used to protect vulnerable infrastructures using automated shutdown procedures, to stop high velocity trains and to save lives if the general public is notified about imminent strong ground shaking. In this paper, we describe the aims and operation of seismological networks, covering in more detail the early warning systems. Then we delineate the seismotectonic settings and seismicity in Hungary and Kazakhstan, furthermore, describe the operating seismological networks and the related scientific research areas with emphasis on civil protection. Hungary and Kazakhstan differ not only in the size of their territory, but also in their seismicity, therefore, in addition to the similarities, there are also significant differences between the aims and problems of their seismological networks.

Assessment of Flood Early Warning Systems with Social Media

2021 ◽  
Author(s):  
Thierry Hohmann ◽  
Judit Lienert ◽  
Jafet Andersson ◽  
Darcy Molnar ◽  
Peter Molnar ◽  
...  
Keyword(s):  
Social Media ◽  
Performance Assessment ◽  
Early Warning ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Economic Losses ◽  
Warning Systems ◽  
Flood Events ◽  
Spatial Coverage ◽  
De Bruijn

<p><strong>Introduction</strong></p><p>Flood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.</p><p> </p><p><strong>Research Approach</strong></p><p>FANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.</p><p> </p><p><strong>Findings</strong></p><p>The detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, POD<sub>FEDA</sub> and FAR<sub>FEDA</sub> (deduced from FEDA) were of similar order of magnitude as the POD<sub>S</sub> and FAR<sub>S</sub> (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.</p>

scholarly journals Communication Structures and Decision Making Cues and Criteria to Support Effective Drought Warning in Central Malawi

2020 ◽  
Vol 2 ◽  
Author(s):  
Alexia Calvel ◽  
Micha Werner ◽  
Marc van den Homberg ◽  
Andrés Cabrera Flamini ◽  
Ileen Streefkerk ◽  
...  
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Warning System ◽  
Agricultural Practices ◽  
Early Warning Systems ◽  
Sudden Onset ◽  
Small Scale ◽  
Warning Systems ◽  
Structured Interviews ◽  
Early Action

Early warning systems trigger early action and enable better disaster preparedness. People-centered dissemination and communication are pivotal for the effective uptake of early warnings. Current research predominantly focuses on sudden-onset hazards, such as floods, ignoring considerable differences with slow-onset hazards, such as droughts. We identify the essential factors contributing to effective drought dissemination and communication using the people-centered approach advocated in the WMOs Multi-Hazard Early Warning System Framework (MHEWS). We use semi-structured interviews with key stakeholders and focus group discussions with small-scale farmers in the Mangochi and Salima Districts of Malawi. We show that the timely release of seasonal forecast, the tailoring of the drought warning content (and its timing) to agricultural decision making, and the provision of several dissemination channels enhance trust and improve uptake of drought warning information by farmers. Our analysis demonstrates that farmers seek, prepare, and respond to drought warning information when it is provided as advice on agricultural practices, rather than as weather-related information. The information was found to be useful where it offers advice on the criteria and environmental cues that farmers can use to inform their decisions in a timely manner. Based on our findings, we propose that by focusing on enhancing trust, improving information uptake and financial sustainability as key metrics, the MHEWS can be adapted for use in monitoring the effectiveness of early warning systems.

scholarly journals Climate Risk and Early Warning Systems: Adaptation Strategies for the Most Vulnerable Communities

2021 ◽  
Vol 18 (02) ◽  
Author(s):  
Jessica Bhardwaj ◽  
Atifa Asghari ◽  
Isabella Aitkenhead ◽  
Madeleine Jackson ◽  
Yuriy Kuleshov
Keyword(s):  
Early Warning ◽  
Climate Adaptation ◽  
Early Warning Systems ◽  
Adaptation Strategies ◽  
Climate Risk ◽  
Economic Losses ◽  
Natural Environments ◽  
Warning Systems ◽  
Vulnerable Communities ◽  
Policy And Planning

Climate risk and resultant natural disasters have significant impacts on human and natural environments. It is common for disaster responses to be reactive rather than proactive due to inadequate policy and planning mechanisms—such reactive management responses exacerbate human and economic losses in times of disaster. Proactive disaster responses maximize disaster resilience and preparation efforts in non-disaster periods. This report focuses on proactive, localized, and inclusive adaptation strategies for addressing impacts of three natural hazards: drought, floods, and tropical cyclones. Four key synergistic climate adaptation strategies are discussed—Post Disaster Reviews, Risk Assessments, Early Warning Systems and Forecast-based Financing. These strategies are further supported with a number of case studies and recommendations that will be of assistance for policymakers in developing evidence-based adaptation strategies that support the most vulnerable communities in the transition towards regarding disaster as a risk as opposed to a crisis.

scholarly journals Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**

2021 ◽  
Vol 15 (02) ◽  
pp. 11-17
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Sidi Ahmed Mahmoudi ◽  
Adriano Guttadauria ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Warning ◽  
Large Scale ◽  
Prediction Models ◽  
Warning System ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Warning Systems ◽  
Large Scale Data ◽  
Set Up

Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

scholarly journals Geomorphology-Based Analysis of Flood Critical Areas in Small Hilly Catchments for Civil Protection Purposes and Early Warning Systems: The Case of the Feltrino Stream and the Lanciano Urban Area (Abruzzo, Central Italy)

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2228
Author(s):  
Tommaso Piacentini ◽  
Cristiano Carabella ◽  
Fausto Boccabella ◽  
Silvia Ferrante ◽  
Carlo Gregori ◽  
...  
Keyword(s):  
Urban Area ◽  
Early Warning ◽  
Drainage Basin ◽  
Central Italy ◽  
Warning System ◽  
Early Warning Systems ◽  
Urban Stream ◽  
Civil Protection ◽  
Critical Areas ◽  
Basin Scale

This work is based on a drainage basin-scale geomorphological investigation combined with flood modeling. It is focused on the assessment of flood critical areas for the implementation of a geomorphology-based urban Early Warning System (EWS) in the urban area of Lanciano and the Feltrino Stream basin (a minor coastal basin of the Abruzzo hills, Central Italy). This area was investigated by combining: pre-existing geological, geomorphological, and hazard data and new detailed field surveys and mapping of geomorphological and hydrographical features (superficial and buried natural and urban stream network). The study was integrated with 2D flood numerical modeling for verifying the expected flooded areas and calibrating the critical areas. All the collected data were integrated into a geodatabase, and an expert-based approach through a geomorphology-based matrix allowed us to define the main categories of flood critical areas. The assessment of the critical areas supported the emplacement of a network of rainfall, temperature, and flood gauges. The geodatabase, the derived critical areas, and the gauge network contributed to set up an urban EWS, integrated with the regional forecast-based warning system. This system provides combined forecast-based, rainfall threshold-based, and flood monitoring-based alerts for floods. It incorporates communication tools for civil protection management. Finally, the EWS provides a tool for civil protection purposes and for the management of flood critical areas and the mitigation of the related risks by local authorities and will be integrated with sensors related to other hazards (i.e., landslides, wind, etc.).

scholarly journals A Study on the Slope Failure Monitoring of a Model Slope by the Application of a Displacement Sensor

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Sungyong Park ◽  
Hyuntaek Lim ◽  
Bibek Tamang ◽  
Jihuan Jin ◽  
Seungjoo Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Slope Failure ◽  
Early Warning Systems ◽  
Displacement Sensor ◽  
Scale Model ◽  
Economic Losses ◽  
Real Time Monitoring ◽  
Landslide Occurrence ◽  
Model Slope

Many causalities and economic losses are caused by natural disasters, such as landslides and slope failures, every year. This suggests that there is a need for an early warning system to mitigate casualties and economic losses. Most of the studies on early warning systems have been carried out by predicting landslide-prone areas, but studies related to the prediction of landslide occurrence time points by the real-time monitoring of slope displacement are still insufficient. In this study, a displacement sensor and an Internet of Things (IoT) monitoring system were combined together, to monitor slope failure through cutting experiments of a real-scale model slope. Real-time monitoring of the slope movement was performed simultaneously via a low-cost, efficient, and easy-to-use IoT system. Based on the obtained displacement data, an inverse displacement analysis was performed. Finally, a slope instrumentation standard was proposed based on the slope of the inverse displacement for early evacuation before slope failure.

Developing a South‐Eastern European Multi‐Hazard Early Warning Advisory System

2020 ◽  
Author(s):  
Fredrik Wetterhall ◽  
Umberto Modigliani ◽  
Milan Dacic ◽  
Sari Lappi
Keyword(s):  
International Development ◽  
Data Storage ◽  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Limited Area ◽  
Meteorological Forcing ◽  
Advisory System ◽  
The World

<p>The project “South-East European Multi-Hazard Early Warning Advisory System” (SEE-MHEWS-A) is a collaborative effort to strengthen the existing early warning capacity in the region. The project was initiated in 2014 by the World Meteorological Organization (WMO), and has been supported by the U.S. Agency for International Development (USAID) and the World Bank. The project will test a prototype of a flood early warning system using local information and multiple models to better characterize the flood risk in selected catchments. The aims of the project are: (1) is to strengthen regional co-operation by leveraging national, regional and global capacities to develop improved hydrometeorological forecasts, advisories and warnings to save lives and limit economic losses, (2) strengthen national multi-hazard early warning systems by making tools and data available to the participating countries and other beneficiaries, (3) implement impact-based forecasts and risk-based warnings utilizing non-deterministic hydrometeorological modeling to support governments, disaster management authorities, humanitarian agencies, non-governmental organizations, and other stakeholders in their decision-making and actions, and (4) to harmonise forecasts and warnings in trans-boundary areas. During 18 month the project will setup a full-hydrometeorological forecasting system, including observational data storage and sharing, limited area modelling of the meteorological forcing data and hydrological forecasting.</p>

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