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Risk analysis is the process of identifying and analyzing potential issues that could negatively impact key business initiatives or critical projects in order to help .
Table of contents
- Risk Analysis - Wiley Online Library
- Follow journal
- Introduction to Risk Analysis
- Deterministic Risk Analysis – “Best Case, Worst Case, Most Likely”
SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.
This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year.
Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal. Evolution of the number of total citation per document and external citation per document i.
International Collaboration accounts for the articles that have been produced by researchers from several countries. Safran Risk gives you step-through capabilities to validate the risk model and presents the results in clear, modern, graphical reports. You can even capture an exact picture of how each simulation affected the schedule after completion of your analysis, then export your results to populate your own reports. In addition to the standard risk distribution histogram and tornado charts, Safran Risk offers innovative reports to provide additional insight into risk impacts:.
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Building a risk model can be a time-consuming process.
By providing instant feedback on the relevant impacts as the model is being built, the real-time analyser in Safran Risk not only speeds up the process but also makes it a more interactive experience. Safran Risk is unique in allowing users to create risk calendars from existing time series data. This addresses one of the major concerns with any risk analysis, the validity of the input information, and maximises your investment.
When this information is not available, risk calendars can also be created from templates used to define the relevant downtime periods. For project driven organisations looking to improve understanding of project risk and the impact on schedule and cost, Safran Risk is the leading solution. The classification is based on 20th percentiles.
Panels b — f presents the exposure for the four income groups per hazard and per hazard intensity band.
See Methods for further discussions on the justification on these hazard bands. For river and coastal flooding, however, due to higher flood protection standards 15 , High income countries have fewer kilometers exposed. For tropical cyclones and earthquakes, the large share of exposed infrastructure in upper middle income and high income countries is predominantly caused by the geographic occurrence of the hazard.
Many of the areas of highest exposure in Fig. This is clearly visible in Fig. Earthquake is, for instance, the dominant hazard along the San Andreas Fault and the coastline of Chile and Peru. Dominant hazard per region. This figure presents the hazard causing the highest transport infrastructure exposure in each region. The pie chart shows the relative percentage of land area excluding areas with insufficient data where that specific hazard causes the highest exposure. A further exploration of Fig. Europe and central North America, on the other hand, see a predominant exposure to surface flooding.
At a country level, results show that multi-hazard exposure in absolute terms is highest in Japan and China. In relative terms, South Sudan 2. These high levels of exposure are primarily driven by fluvial flooding and cyclones for, respectively, South Sudan and Madagascar. The global EAD to transport infrastructure assets are presented in Fig. These represent direct damages to road and rail assets, and do not include the costs from transport delays and disruption, or wider economic impacts. The total global EAD for all hazards combined ranges from 3. These values represent between 0.
The mean EAD for transport infrastructure assets is Total EAD per hazard. Panel a shows the relative distribution of the total EAD to infrastructure assets among the different hazards. Panel b shows the calculated range of total multi-hazard global EAD. Panel c shows the calculated range of the EAD per hazard. Tropical cyclones cause relatively fewer damages compared to their exposure—there are twice as many kilometers of infrastructure exposed to high intensity cyclones than to coastal floods Fig.
Risk Analysis - Wiley Online Library
This is because the impact of cyclone winds is largely limited to bridge damage and the cost of removing trees fallen on road carriageways and railway tracks see Methods and Supplementary Fig. Overall, the EAD in this study are higher compared to the few available estimates on global risk to infrastructure assets.
In our view, this is mainly due to the high-resolution representation of infrastructure assets in our study, instead of using a proxy representation as done in Alfieri et al. Intuitively, one would expect exposure of transport infrastructure to natural hazards to increase with income under the assumption that countries accumulate more infrastructure as GDP increases.
However, high income countries only bare approximately a quarter of the global risk, while upper middle income countries bare half of the global risk and lower middle income almost a third. This is because as countries move from upper middle income to high income, they invest more in higher protection standards of flood defense Expected annual damages per hazard.
The upper row of boxplots presents the absolute EAD for each individual hazard and, in the right-most boxplot, the total multi-hazard EAD. The lower row of boxplots presents the EAD per kilometer of infrastructure for each individual hazard and, in the right-most boxplot, the total multi-hazard EAD per kilometer. To further explore this and to control for the difference in infrastructure length, we analyze the total EAD per kilometer of infrastructure Fig. We find a steep increase in total risk per kilometer from low income countries to lower middle income countries, and then a decrease as countries income grows.
This bell-shaped curve peaking around the lower middle-income level for total risk per kilometer is largely due to surface and coastal flooding, followed by earthquakes. This can also be observed from Fig. These results recall the Kuznet curve for environmental degradation As countries grow richer, they invest in more infrastructure, which increases disaster exposure and environmental degradation in the case of the initial Kuznet theory Absolute disaster damages thus increase as more infrastructure is built.
After they reach a higher level of income in the middle income category , they have enough resources to prioritize higher resilience and they reduce the vulnerability of their infrastructure assets through investments in more rigorous design standards for transport assets standards and increased flood protection. Road bridges also play an important role in total EAD, for all hazards except surface flooding. The 20 countries in which the transport infrastructure is most affected by natural hazards are presented in Fig. When looking at EAD in absolute terms, as shown in Fig.
Generally, the countries in this list have either a very high exposure to multiple hazards or high value infrastructure. Supplementary Materials provide an overview of these numbers for each country considered in this study. Multi-hazard risk per country. Panel a presents the 20 countries that have the highest multi-hazard EAD in absolute terms. Panel d presents the twenty countries that have the highest multi-hazard EAD per kilometer of infrastructure in that country.
Supplementary Materials provide the underlying data for panels a — d for all countries. Interestingly, the EAD as percentage of the total infrastructure value in a country Fig. In particular, the list contains several small islands developing states SIDS. This suggests that these countries have a relatively large amount of high value transport assets exposed to multi-hazards, compared to the global average. This is because in SIDS, most of the available land is exposed to multiple hazards. Several countries appear in three panels in Fig.
Myanmar, for instance, is experiencing one of the highest absolute levels of risk to its transport infrastructure, but also the highest risk as a percentage of GDP and one of the highest per kilometer of road. For Papua New Guinea, this means that not only its estimated infrastructure value is relatively high compared to its GDP, but also that a relatively high share of its infrastructure is exposed to multiple hazards. This indicates that for these countries, the infrastructure at risk is predominately of higher value. Overall, the countries in this study which we identify as most vulnerable are consistent with the reported global economic losses and human cost of disasters between and 7.
This would be a 0. Risk reduction due to flood design-standard upgrades. Panel a presents the total combined reduction in EAD per region. Panel b presents the absolute reduction in EAD for coastal, surface and river flooding. Panel c presents the relative reduction in EAD for coastal, surface, and river flooding. In absolute terms Fig. In relative terms Fig. This large gain is primarily due to higher inundation depths for coastal flooding compared to surface and river flooding Fig.
In spatial terms Fig. Lowest reductions are mainly observed in North America and South Africa. When comparing Fig. The opposite is true for the areas with the lowest reductions. Assessing the cost of providing higher flood protection for roads and railways globally is challenging. However, for some existing paved roads, increasing standards would mean rebuilding road sections almost entirely to replace culverts and drains.
It thus would not usually make sense to upgrade the standard until the road requires a major rehabilitation. Modern design practice can ensure resistance to wind damage in all but the severest of cyclones. Despite the difficulties, it is interesting to get a sense of the potential benefit-cost ratio of upgrading roads to reduce the risk of flooding to road assets.
To do so, we perform a cost-benefit analysis on each road segment CBA, Methods. For roads that are not exposed to any hazard, such an investment does not have any benefit and thus has negative returns. We find that such an improvement only has a BCR higher than 1 for 4. This is not surprising given that only 7. Important to emphasize is that in this study, we only focus on the direct asset damages. When including network disruptions and the wider economic impacts, total avoided losses are expected to increase, making investments in adaptation potentially more beneficial in more places.
These results highlight the value of having hazard information for designing roads, which makes it possible to target improvements on exposed roads only. An inherent problem with global studies, and for disaster risk modeling on this scale in particular, is the large number of assumptions required to make in such a data-scarce analysis. As addressed by Ward et al. For flood risk analysis in particular, a large part of the uncertainty is on the hazard side.
As we do not create any hazard maps in this study, we focus on the quantification of the uncertainties in the loss estimation. As shown in De Moel and Aerts 23 , large uncertainties arise in maximum loss values and the shape of the fragility curves.
According to De Moel et al. Supplementary Fig. For road and railway assets, we find similar results across the hazards. Across earthquakes and floods coastal, surface, and river , road carriageway damages are particularly sensitive to the choice of fragility curve and the assumed repair costs. Reducing this uncertainty is particularly challenging as it would require location-specific damage curves and repair costs, which depend on local geographic and economic circumstances. For example, repair costs depend on the efficiency of the local transport authorities and the local cost of raw materials.
Despite the difficulties, these geographically varying fragility curves should be developed in the future to reduce uncertainty and improve the damage estimates.
Introduction to Risk Analysis
This is the first study to have quantified the global risk to transport infrastructure assets for multiple natural hazards. We have used state-of-the art global hazard mapping, combined with innovative analysis of approximately 50 million km of transport network data included in OSM, and assumptions about the fragility of transport infrastructure derived from a variety of sources. The study demonstrates the potential for conducting infrastructure risk analysis at a high spatial resolution on a global scale.
Sensitivity analysis has revealed the importance of understanding asset fragility. The results for overall transport infrastructure exposure and risk are broadly in line with previous risk analyses of natural hazards e. At the global level, EAD are small compared to the budget required for maintaining reliable transport networks 0. One might thus conclude that building more resilience is further down the list of priorities, after ensuring sustainable sources of funding for regular maintenance.
However, our results reveal geographical disparities in exposure and risk, with for example the particular vulnerability of transport infrastructure in small island developing states.
Deterministic Risk Analysis – “Best Case, Worst Case, Most Likely”
In other words, we find that for several countries and regions, investing in transport asset resilience should be a priority. Of course, care should be taken with the interpretation of these results, as local road conditions are unknown in this study and a generalized approach is taken. Nonetheless, it is clear that there are significant benefits to be gained from improving the resilience of exposed transport infrastructure.
These are expected to be low-regrets investments in the context of a changing climate 8 , 9 , There is a need for better risk information to avoid spending more on all assets, but being able to spatially target improvements. The economic and social benefits to be gained from doing so would go well beyond direct infrastructure damage. Indeed, studies 28 , 29 , 30 , 31 that estimate the economic impact of disasters through transport-economic models that account for the impact of transport interruption on the ability of supply chains to maintain production, conclude that indirect losses as a result of infrastructure failure represent a large share of the total cost of disasters.
An overview of the approach taken is presented in Supplementary Fig. Due to the large size of all data sources both in storage and in information , we have split the analysis over 46, regions based on the GADM administrative level 1 and 2 datasets By using parallel and cloud computing, runtimes have been brought down to a reasonable time-scale, allowing for a global risk analysis with this level of detail.
The remainder of this section will explain the analysis in depth. This study includes earthquakes, tropical cyclones, and surface, river and coastal flooding. For tropical cyclones, we take a similar approach using the Saffir—Simpson scale There are no widely recognized intensity bands available for floods, so these bands are based on empirical evidence of loss intensity.
In recent major earthquakes e. With direct shaking damage to road and rail expected to be minimal, we adopt liquefaction susceptibility as a proxy for potential road and rail damage across the different studied return periods. Damages can range from superficial with minimal clean-up costs, to complete replacement 34 , The likelihood of surface rupture damage to assets in close proximities to fault lines and permanent ground displacements are not considered herein. As state-of-practice in situ testing for assessing liquefaction potential is not feasible at the global scale, we adopt the geospatial prediction models of Zhu et al.
The models relate common ground-motion intensity measures with geospatial parameters relevant to liquefaction susceptibility. Calibrated to 27 earthquake events, the models have since shown promising predictive capacity at high resolutions For our global susceptibility model we combine the inland and coastal models of Zhu et al. Liquefaction susceptibility is computed at a 1. Other required datasets are collated for: rivers 40 , 41 , 42 , ground water 43 , precipitation 44 , and land mass Susceptibility is grouped into five classes: very low, low, moderate, high, and very high 34 , The compiled dataset is available in the supplementary material Supplementary Fig.
The hazard maps are an output of probabilistic seismic hazard analysis with global coverage. The coarse resolution of the analysis allows for global coverage, but a trade-off is that it limits the consideration of local or unknown faults not previously captured in historical catalogs, therefore they could underestimate hazard locally in some areas. The maps are an output of probabilistic cyclone analysis based on perturbation of historical cyclone tracks and wind-field modeling.
The data does not include the effects of extratropical cyclones or convective storms in these basins or other areas. River caused by rivers overtopping their banks and surface caused by extreme local rainfall flood hazards are represented by the Fathom Global pluvial and fluvial flood hazard dataset This is a 3-arcsecond c. We apply the undefended flood hazard maps, which do not consider the effects of flood protection on inundation.