what are the two primary methods used to create a risk assessment?

Past: C.J. van Westen

Introduction

Risk is defined as the probability of harmful consequences, or expected losses (deaths, injuries, property, livelihoods, economic activity disrupted or environment damaged) resulting from interactions betwixt natural or human-induced hazards and vulnerable weather (UN-ISDR, 2009, EC, 2011). Run a risk can presented conceptually with the post-obit bones equation indicated in Figure i

Effigy 1: Schematic representation of take a chance as the multiplication of adventure, vulnerability and quantification of the exposed elements-at-risk. The diverse aspects of hazards, vulnerability and elements-at-risk and their interactions are too indicated. This framework focuses on the analysis of concrete losses, using physical vulnerability data.

Risk assessment is a process to determine the probability of losses by analyzing potential hazards and evaluating existing weather condition of vulnerability that could pose a threat or impairment to belongings, people, livelihoods and the environment on which they depend (UN-ISDR, 2009). ISO 31000 (2009) defines risk cess as a process made up of three processes: risk identification, chance analysis, and risk evaluation. Risk identification is the process that is used to find, recognize, and draw the risks that could bear upon the achievement of objectives. Risk analysis is the process that is used to sympathize the nature, sources, and causes of the risks that have been identified and to estimate the level of risk. It is also used to study impacts and consequences and to examine the controls that currently exist. Chance evaluation is the process that is used to compare risk analysis results with risk criteria in order to determine whether or not a specified level of risk is acceptable or tolerable.

Risk mapping for natural run a risk risk tin be carried out at a number of scales and for different purposes. Tabular array 1 gives a summary. In the post-obit sections four methods of take chances mapping will exist discussed: Quantitative hazard assessment (QRA), Issue-Tree Analysis (ETA), Risk matrix approach (RMA) and Indicator-based approach (IBA).

Table 1: Indication of scales of analysis with associated objectives and data characteristics (approaches: QRA = Quantitative risk cess, EVA = Event-Tree Assay, RMA = Risk matrix arroyo, IBA = Indicator-based approach)

Scale of assay	Calibration	Possible objectives	Possible approaches
International, Global	< ane : i meg	Prioritization of countries/regions; Early on warning	Simplified RMA & IBA
Small: provincial to national scale	< ane:100,000	Prioritization of regions; Analysis of triggering events; Implementation of national programs; Strategic environmental cess; Insurance	Simplified EVA, RMA & IBA
Medium: municipality to provincial level	1:100000 to 1:25000	Analyzing the effect of changes; Analysis of triggering events; Regional development plans	RMA / IBA
Local: customs to municipality	1:25000 to 1:5000	State use zoning; Analyzing the effect of changes; Environmental Bear upon Assessments; Design of risk reduction measures	QRA / EVA / RMA IBA
Site-specific	1:5000 or larger	Design of risk reduction measures; Early alert systems; detailed land utilise zoning	QRA / EVA / RMA

Figure ii: Components relevant for run a risk assessment, and the four major types of hazard mapping that are presented in this department.

Quantitative Hazard Cess

If the various components of the risk equation can be spatially quantified for a given set of gamble scenarios and elements-at-risk, the take a chance can exist analyzed using the following equation:

In which:

P_(T│HS) = the temporal probability of a certain run a risk scenario (HS). A chance scenario is a adventure event of a certain type (e.thousand. flooding) with a sure magnitude and frequency;

P_(South│HS) = the spatial probability that a particular location is affected given a sure adventure scenario;

A_(ER│HS) = the quantification of the amount of exposed elements-at-risk, given a sure hazard scenario (e.g. number of people, number of buildings, budgetary values, hectares of state) and

V_(ER│HS) = the vulnerability of elements at run a risk given the hazard intensity nether the specific hazard scenario (as a value between 0 and 1).

The method is schematically indicated in Figure 3. GIS operations are used to analyze the exposure every bit the intersection between the elements-at-risk and the chance footprint expanse for each chance scenario. For each element-at-risk also the level of intensity is recorded through a GIS-overlay performance. These intensity values are used in combination with the chemical element-at-chance type to notice the corresponding vulnerability curve, which is and so used as a lookup table to find the vulnerability value. The way in which the amount of elements-at-risk are characterized (e.grand. as number of buildings, number of people, economic value) besides defines the way in which the hazard is calculated. The multiplication of exposed amounts and vulnerability should exist done for all elements-at-risk for the aforementioned hazard scenario. The results are multiplied with the spatial probability that the chance footprint really intersects with the element-at-risk for the given hazard scenario P_(South│HS) to account for uncertainties in the hazard modelling. The resulting value represents the losses, which are plotted against the temporal probability of occurrence for the same risk scenario in a then-called risk curve. This is repeated for all bachelor hazard scenarios. At to the lowest degree three individual scenarios should be used, although it is preferred to use at to the lowest degree 6 events with unlike return periods (FEMA, 2004) to ameliorate represent the risk curve. The surface area under the curve is and then calculated by integrating all losses with their respective annual probabilities. It is possible to create chance curves for the unabridged report expanse, or for different spatial units, such as authoritative units, census tracks, road or railway sections etc.  Hazard tin be presented in a number of unlike ways, depending on the objectives of the risk cess (Birkmann, 2007). Risk tin can expressed in absolute or relative terms. Absolute population run a risk can exist expressed as individual chance (the annual probability of a single exposed person to be killed) or as societal run a risk (the relation between the annual probability and the number of people that could be killed). Accented economical risk tin can exist expressed in terms of Average Annual Loss, Maximum Probable Loss, or other indices that are calculated from a series of loss scenarios, each with a relation between frequency and expected monetary losses (Jonkman, van Gelder and Vrijling, 2002)

Figure three:  Schematic representation of Quantitative Take a chance Assessment. Click to enlarge.

The components that are involved in risk assessment take a loftier caste of uncertainty. Aleatory uncertainty is associated with the variation of the input data used in the risk assessment. For case the variations in soil characteristics used to model landslide probability, surface characteristics, building characteristics etc. These are normally incorporated in probabilistic risk analysis (Bedford and Cook, 2001) which calculates thousands of take chances and take a chance scenarios taking the variations of the input factors and computing exceedance probabilities using techniques such as Monte Carlo simulation. Epistemic dubiety refers to uncertainty associated with incomplete or imperfect cognition about the processes involved, and lack of sufficient data. This is oft a serious problem every bit in that location may non exist enough data bachelor to make up one's mind individual run a risk scenarios, or there are no vulnerability curves for the types of elements-at-risk within the study expanse. Probabilistic take a chance cess takes into account all possible hazard scenarios and the uncertainty of the input factors, past running thousands of loss scenarios, and calculate eventually the loss exceedance curve.  For a number of hazards, such as landslides or flooding, it is very complicated to develop a large number of hazard scenarios due to the large epistemic dubiousness caused past lack of data. In such cases uncertainty can be taken into business relationship using the method illustrated in Figure iv. In this method data are used showing the range of possible values for the temporal probability, spatial probability, intensity of the hazard, value of the elements-at-gamble and vulnerability. The uncertainty range in the temporal probability of the run a risk scenario is reflected by a range of possible values on the Y-axis of the risk curve. The incertitude in the hazard intensity (e.m. h2o height for flooding, bear on pressure for landslides) combined with the uncertainty in the vulnerability curve will results in larger dubiety ranges in vulnerability, which are then multiplied with the uncertainty range of the quantification of elements-at-adventure (e.g. building costs). This then gives a range of values for the expected losses. So instead of a unmarried point in the gamble curve, each adventure scenario volition result in a rectangle, defined by the variation in probability and losses. The upper right corners of the rectangle are connected to provide the most pessimistic take a chance curve, and the lower right corners are connected to provide the virtually optimistic take chances curve. When calculating the surface area under the curves it is so possible to prove the range in annual expected losses.

Figure 4: Method for including uncertainty in Quantitative Chance Assay in cases where it is non possible to define many hazard scenarios. Click to enlarge.

The animation below gives an case of a quantitative risk assessment for buildings that are threatened to flooding with three different scenarios, each with a different probability of occurrence (ii years, ten years and 50 years). In this unproblematic example there are 3 elements at risk only (buildings) that are of 2 types. Type-one buildings are weaker in construction than type-ii buildings. Based on past occurrences of flooding a relation has been made between the h2o depth and the degree of harm using vulnerability curves (explained in chapter five, Section 5.3). This means that with the same water depth type-i buildings will suffer more damage than type-2 buildings. The vulnerability curves presented in animation are hypothetical ones, but are the crucial component in the run a risk assessment. The 3 take a chance scenarios volition affect the iii buildings in a different way. The minor table in the animation indicates the water depth that can exist expected for the three houses related to the 3 scenarios

Figure 5: Step-by-step blitheness of hazard assessment illustrated for flooding. Click to outset the animation in a new window

Result-tree approaches

A number of hazard may occur in chains: one hazard causes the next. These are likewise chosen domino effects, or concatenated hazards. These are the most problematic types to analyze in a multi-hazard run a risk cess. The best approach for analyzing such hazard chains is to employ so-chosen event-copse. An result tree is a system which is applied to clarify all the combinations (and the associated probability of occurrence) of the parameters that bear on the system under analysis. All the analyzed events are linked to each other by means of nodes (See Figure half dozen) all possible states of the system are considered at each node and each state (branch of the consequence tree) is characterized by a defined value of probability of occurrence.

Figure half dozen: Schematic representation of an event-tree assay.

The figure below gives an examples of an event tree for a situation where a rockfall in a lake may trigger a inundation wave that would affect a village (from Lacasse et al., 2008)

Effigy 7: Bayesian Event tree for seismic sea wave propagation, given that rock slide in Aknes has occurred (V= rockslide volume, R=run-up height). From Lacasse et al., 2008) Click to enlarge.

Risk matrix approach

Take a chance assessments are often complex and do non allow to develop a total numerical approach, since many aspects are non fully quantifiable or have a very large degree of dubiousness. This may be related to the difficulty to ascertain hazard scenarios, map and characterize the elements-at-hazard, or define the vulnerability using vulnerability curves. In gild to overcome these problems the adventure is oftentimes assessed using so-called risk matrices or consequences-frequency matrices (CFM), which are diagrams with event and frequency classes on the axes (Meet Figure eight). They allow to classify risks based on expert knowledge with limited quantitative data (Haimes, 2008; Jaboyedoff et al., 2014). The risk matrix is made of classes of frequency of the hazardous events on one axis, and the consequences (or expected losses) on the other axis.  Instead of using fixed values, the use of classes allows for more flexibility and incorporation of practiced opinion.  Such methods accept been practical extensively in natural hazard take chances assessment, east.g. in Switzerland (Jaboyedoff et al., 2014). This arroyo too permits to visualize the effects and consequences of chance reduction measures and to requite a framework to understand risk assessment.  The organisation depends on the quality of the group of experts that are formed to identify the run a risk scenarios, and that carry out the hazard filtering and ranking in several sub-stages characterized by frequency (probability) and impact classes and their corresponding limits (Haimes, 2008).

Figure 8: Case of the chance matric approach, which consists of a matrix with classes of frequency (of the hazardous outcome) and the consequences.

Figure 9: Example of potential building area in a high take a chance area and illustration of the proposed solutions. The chance matrix is used to represent the degree of take a chance. The scope of tolerable risk (yellow) is between the limits of tolerance and of acceptability. The initial state of affairs 0 is a combination of very high frequency of droppings flows with a high impact. After construction of a deflection dike or wall the frequency doesn't modify simply the touch on decreases considerably. The areas Z1 and Z2 on the other hand will become a college frequency of occurrence and college result equally a outcome of the mitigation works (Jaboyedoff et al., 2014). Click to enlarge.

Indicator-based Arroyo

There are many situations where the (semi)-quantitative methods for chance mapping are non appropriate. This could be because the information are lacking to exist able to quantify the components, such as hazard frequency, intensity, and concrete vulnerability. For instance when the risk assessment is carried out over large areas, or in areas with limited data. Another reason is that ane would like to take into account a number of different components of vulnerability that are not incorporated in (semi-) quantitative methods, such as social vulnerability, environmental vulnerability and chapters. In those cases it is common to follow an indicator-based approach to mensurate run a risk and vulnerability through selected comparative indicators in a quantitative manner in lodge to be able to compare dissimilar areas or communities. The procedure of disaster risk assessment is divided into a number of components, such as hazard, exposure, vulnerability and chapters (See Effigy 10), through a so-called criteria tree, which list the subdivision into objectives, sub-objectives and indicators. Data for each of these indicators are collected at a detail spatial level, for instance past authoritative units. These indicators are then standardized (e.1000. by reclassifying them between 0 and 1), weighted internally within a sub-objective and then the diverse sub-objectives are besides weighted among themselves. Although the individual indicators normally consist of quantitative information (due east.1000. population statistics), the resulting vulnerability, hazard and gamble results are scaled between 0 and i. These relative data allows to compare the indicators for the various administrative units. These methods can be carried out at different levels, ranging from local communities (due east.thou. Bollin and Hidajat, 2006) cities (Greiving et al, 2006) to countries (Van Westen et al., 2012).

Effigy x: Run a risk Indicator based arroyo.

The approaches are by and large based on the development of then-called risk indices, and on the use of spatial multi criteria evaluation. One of the first attempts to develop global hazard indicators was washed through the Hotspots projection (Dilley et al. 2005). In a written report for the Inter-American Development Bank, Cardona (2005) proposed different sets of complex indicators for benchmarking countries in different periods  and to make cross-national comparisons. 4 components or composite indicators reflect the principal elements that represent vulnerability and show the advances of different countries in risk management: Disaster Arrears Index, Local Disaster Index, Prevalent Vulnerability Index and Adventure Management Index. Each alphabetize has a number of variables that are associated with it and empirically measured. The DDI can exist considered as an indicator of a country's economic vulnerability to disaster. Information on using the Disaster Risk Alphabetize for Belize can found here: https://world wide web.imf.org/external/np/seminars/eng/2013/caribbean/pdf/be .Peduzzi et al. (2005; 2009) have adult global indicators, non on the basis of administrative units, but based on gridded maps. The Disaster Risk Index (Un-ISDR, 2005b) combines both the full number and the percentage of killed people per country in large- and medium-scale disasters associated with droughts, floods, cyclones and earthquakes. In the DRI, countries are indexed for each take a chance type according to their degree of concrete exposure, their degree of relative vulnerability, and their degree of risk. Also at local scale hazard indices are used, ofttimes in combination with spatial multi criteria evaluation (SMCE). Castellanos and Van Westen (2007) nowadays an example of the use of SMCE for the generation of a landslide adventure index for the land of Republic of cuba, generated by combining a hazard index and a vulnerability index. The adventure index is made using indicator maps related to triggering factors (earthquakes and rainfall) and environmental factors. The vulnerability index was made using v key indicators: housing status and transportation (physical vulnerability indicators), population (social vulnerability indicator), product (economic vulnerability indicator) and protected areas (environmental vulnerability indicator).  The indicators were based on polygons related to political-administrative areas, which are mostly at municipal level. Each indicator was processed, analysed and standardized according to its contribution to risk and vulnerability. The indicators were weighted using direct, pair wise comparison and rank ordering weighting methods and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region and administrative units at provincial and municipal levels. Some other instance at the local level is presented past Villagrán de León (2006) which incorporates 3 dimensions of vulnerability, the calibration or geographical level (from human being to national level), the various sectors of society, and 6 components of vulnerability. The method uses matrices to calculate a vulnerability index, which was grouped in qualitative classes (high, medium and low).

Figure 11: Example of the utilise of indicators in a Spatial Multicriteria Evaluation for the country of Georgia (Van Westen et al., 2012) Click to enlarge.

Conclusions

The 4 methods for risk cess that were treated in this chapter all have sure advantages and disadvantages, which are summarized in Table 3.  The Quantitative Chance Cess method is the best for evaluating several alternatives for risk reduction, through a comparative assay of the adventure before and after the implementation followed by a cost-benefit analysis. The event-tree assay is the best approach for analyzing complex chains of events and the associated probabilities. Qualitative methods for risk assessment are useful equally an initial screening process to place hazards and risks. They are also used when the causeless level of risk does not justify the time and endeavour of collecting the vast amount of information needed for a quantitative risk assessment, and where the possibility of obtaining numerical data is limited.The take chances matrix approach is ofttimes the most applied approach as basis for spatial planning, where the outcome of adventure reduction methods tin can be seen as changes in the classes within the adventure matrix. The indicator-based arroyo, finally, is the best when in that location is not enough data to carry out a quantitative analysis, but likewise every bit a follow-up of a quantitative analysis as it allows to take into account other aspects than merely concrete harm.

Table ii: Advantages and disadvantages of the 4 risk assessment methods discussed.

Method	Advantages	Disadvantages
Quantitative risk assessment (QRA)	Provides quantitative risk information that can be used in Toll-do good analysis of chance reduction measures.	Very data demanding. Difficult to quantify temporal probability, gamble intensity and vulnerability.
Event-tree analysis	Allow modelling of a sequence of events, and works well for domino furnishings	The probabilities for the unlike nodes are difficult to assess, and spatial implementation is very difficult due to lack of data.
Risk matrix arroyo	Allows to express adventure using classes instead of verbal values, and is a skilful footing for discussing risk reduction measures.	The method doesn't give quantitative values that can be used in price-benefit analysis of risk reduction measures. The assessment of impacts and frequencies is hard, and one expanse might have unlike combinations of impacts and frequencies.
Indicator-based approach	Just method that allows to behave out a holistic risk assessment, including social, economic and environmental vulnerability and capacity.	The resulting risk is relative and doesn't provide information on actual expected losses.

References

ACOE (ARMY CORPS OF ENGINEERS), 2004, Water Resource Assessment of Dominica, Antigua, Barbuda, St. Kitts andNevis: Mobile Commune and Topographic Engineering Center Report, U.S. Army Corps of Engineers, Mobile, AL, 94 p.

Alexander D (2001). Encyclopedia of ecology scientific discipline, Chapter Natural hazards. Kluwer Bookish Publishers

Bedford T, Cooke RM (2001). Probabilistic Run a risk Analysis: Foundations and methods; Cambridge Academy Press

Birkmann J (ed) (2006). Measuring Vulnerability to Natural Hazards: Towards Disaster Resilient Societies. UNU Press, Tokyo, New York.

Birkmann J (2007). Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications. Environmental Hazards 7 (2007) 20–31

Bollin C, Hidajat R (2006). Community-based disaster take a chance alphabetize: pilot implementation in Indonesia. In: Birkmann, J. (Ed.), Measuring Vulnerability to Natural Hazards—Towards Disaster Resilient Societies. UNU-Press, Tokyo, New York, Paris

Breheny, P., 2007, Hydrogeologic/Hydrological Investigation of Landslide Dam and Impounded Lake in the Matthieu River Valley, Commonwealth of Dominica, Westward Indies: Unpublished G.S. Dissertation, University of Leeds, United Kingdom.

Cannon S and DeGraff  J (2009). The increasing wildfire and post-fire debris-menstruum threat in Western U.s., and implications for consequences of climate change. In G Sassa and P Canuti (Eds.) Landslides - disaster risk reduction, Springer Verlag, 177-190

CAPRA (2013). Probabilistic Risk Cess Program URL http://world wide web.ecapra.org/

Carpignano A Golia E Di Mauro C Bouchon South and Nordvik J-P (2009). A methodological approach for the definition of multi-risk maps at regional level: start application. Periodical of Risk Research, 12:513–534

Coppock, JT (1995) GIS and natural hazards: an overview from a GIS perspective. In: Carrara A and Guzzetti F (eds) Geographical Information Systems in Assessing Natural Hazards. Volume five of the series Advances in Natural and Technological Hazards Research pp 21-34

Corominas J, van Westen CJ, Frattini P, Cascini L, Malet J-P, Fotopoulou South, Catani F, van den Eeckhaut Yard, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi South, Tofani V, Hervas J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk : open access. In: Bulletin of applied science geology and the environment IAEG, 73 (2014)two pp. 209-263

Cova TJ (1999) GIS in emergency direction. In Geographical Information Systems: Principles, Techniques, Applications, 2d Edition: Management Issues and Applications, Edited past: Longley (Eds) P. A.

DeGraff, J.5., 1987a. Landslide take chances on Commonwealth of dominica, West Indies-Final Written report. Washington, D.C., Organization of American States.

DeGraff, J.V. 1987b. Geological Reconnaissance of the 1986 landslide activity at Skilful Hope, D'Leau Gommier, and Belvue Slopes, Commonwealth of Dominica, West Indies, Study submitted to: Commonwealth of Dominica and the OAS

DeGraff, J.V., Bryce, R., Jibson, R.W., Mora, S., and Rogers, C.T., 1989, Landslides: Their extent and significance in the Caribbean, Proceedings of the 28th International Geological Congress: Symposium on Landslides

DeGraff, J.Five. 1990a. Post-1987 Landslides on Dominica, West Indies: An Assessment of LandslideHazard Map Reliability and Initial Evaluation of Vegetation Event on Gradient Stability, Submitted to The Commonwealth of Commonwealth of dominica and the OAS

DeGraff, J.5. 1990b, Potential Landslide-Dam Gamble Beneath Freshwater Lake, Commonwealth of Commonwealth of dominica, West Indies, Study submitted to Commonwealth of Dominica and the OAS

DeGraff, J. V., 1991: Determining the significance of landslide activity: Examples from the Eastern Caribbean area. Caribb. Geogr., 3, 31–42.

DeGraff, J. 5., 1995, Natural Hazards Vulnerability of the Dominica Hydroelectric Expansion Projection: Caribbean Disaster Mitigation Project Final Report, Organization of American States, Washington, DC, 26 p.

DeGraff, J. 5., 1997, Technical Assessment of the Landslide-Dam Emergency, Commonwealth of Dominica, W.I.: U.S. Agency for International Development Report, Office of the U.S. Foreign Disaster Aid, Washington, DC, 24 p.

DeGraff, J.Five., James, A. and Breheney, P., 2010. The Germination and Persistence of the Matthieu Landslide-Dam Lake, Commonwealth of dominica, W.I.. Environmental & Engineering Geoscience, Vol. 16, No. two, May 2010, pp. 73–89

DeGraff, J.Five., Romesburg, H.C., Ahmad, R, and McCalpin, J. 2012. Producing landslide-susceptibility maps for regional planning in data-scarce regions. Nat Hazards (2012) 64:729–749

DeGraff, J. Five. and Rogers, C. T., 2003, An unusual landslide dam event in Dominica, W Indies: Landslide News, No. 14/xv, pp. 8–11.

DeGraff JV (2012) Solving the dilemma of transforming landslide chance maps into effective policy and regulations, Nat. Hazards World Syst. Sci., 12, 53-60

Delmonaco G Margottini C and Spizzichino D (2006a). ARMONIA methodology for multi-chance cess and the harmonisation of different natural risk maps. Deliverable 3.1.1, ARMONIA.

Delmonaco Yard Margottini C and Spizzichino D (2006b). Report on new methodology for multi-chance assessment and the harmonisation of different natural adventure maps. Deliverable 3.1, ARMONIA.

de Pippo T Donadio C Pennetta M Petrosino C Terlizzi F and Valente A (2008). Coastal hazard assessment and mapping in Northern Campania, Italian republic. Geomorphology, 97:451–466.

EC (2011). Risk cess and mapping guidelines for disaster management. European Commission Committee staff working newspaper, European Matrimony.

FEMA (2004). HAZUS-MH.  FEMA's Methodology for Estimating Potential Losses from Disasters. US Federal Emergency Management Bureau.  http://world wide web.fema.gov/plan/prevent/hazus/index.shtm

Garcia-Aristizabal A, Marzocchi Westward (2013). Software for multi-gamble assessment. Deliverable 3.5 of the EU Matrix project: New methodologies for multi-gamble and multi-run a risk assessment methods for Europe. URL: http://matrix.gpi.kit.edu/downloads/MATRIX-D3.05.pdf.

Granger Thou, Jones TG, Leiba Grand, Scott One thousand, (1999).Community hazard in Cairns: a multi-hazards risk cess. Technical study, Australian Geological Survey Organisation (AGSO) URL http://www.ga.gov.au/corporate_data/33548/33548.pdf.

Greiving S, Fleischhauer K, Lückenkötter, J. (2006) A Methodology for an integrated take chances assessment of spatially relevant hazards, Journal of Environmental Planning and Direction, 49:1, one-19

Guha-Sapir D, Below R, Hoyois Ph (2016) - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be  – Université Catholique de Louvain – Brussels – Belgium.

Haimes YY (2009) Risk Modeling, Assessment, and Management. tertiary Edition. John Wiley & Sons, 1009 p

IRDR (2014) Integrated Research on Disaster Risk. Peril Classification and Hazard Glossary (IRDR DATA Publication No. one). Beijing: Integrated Research on Disaster Take chances. http://world wide web.irdrinternational.org/wp-content/uploads/2014/04/IRDR_DATA-Project-Study-No.-1.pdf

ISO 31000 Risk Management – Principles and guidelines. URL: http://www.iso.org/iso/domicile/standards/iso31000.htm

Jaboyedoff G, Aye ZC, Derron M-H, Nicolet P, Olyazadeh R. (2014) Using the consequence - frequency matrix to reduce the risk: examples and teaching. International Conference Assay and Management of Changing Risks for Natural Hazards 18-19 November 2014, Padua, Italia

Jonkman, SN, Van Gelder P, and Vrijling H (2002). An overview of quantitative risk measures and their awarding for calculation of flood gamble. λµ13 - ESREL 2002 European Conference

Kappes MS, Keiler M, Von Elverfeldt One thousand, Glade T, (2012). Challenges of analyzing multi-run a risk Chance: A Review. Natural Hazards 64(two), 1925-1958

Lacasse S Eidsvik U Nadim F Hoeg K and Blikra LH (2008). Upshot tree analysis of Aknes stone slide hazard. 4 Geohazards Quebec, 4th Canadian Conf. on Geohazards, 551-557.

Lee K and Rosowsky D (2006). Fragility analysis of woodframe buildings considering combined snowfall and earthquake loading. Structural Safety, 28: 289-303

Luino F (2005). Sequence of instability processes triggered past heavy rainfall in the northern Italian republic. Geomorphology, 66:thirteen–39.

Marzocchi Due west Mastellone M and Di Ruocco A (2009). Principles of multi-risk assessment: interactions amongst natural and man-induced risks. European Commission. URL : http://cordis.europa.eu/documents/documentlibrary/106097581EN6.pdf

OAS (1991) Geographic Information Systems In Natural Risk Management. Organization of American States. Primer on Natural Run a risk Management in Integrated Regional Development Planninghttps://www.oas.org/dsd/publications/Unit/oea66e/ch05.htm

Peila D, Guardini C (2008) Use of the event tree to assess the risk reduction obtained from rockfall protection devices. Nat. Hazards Earth Syst. Sci., 8, 1441–1450, 2008

Perles Roselló K and Cantarero Prados F (2010). Problems and challenges in analyzing multiple territorial risks. methodological proposals for multi-risk mapping. Boletín de la Asociación de Geógrafos Espanoles, 52:399–404

Prinos P (2008). Review of Flood Risk Mapping. European Community Sixth Framework Plan for European Research and Technological Evolution. FLOODsite.

Roberts NJ, Nadim F, Kalsnes B (2009). Quantification of vulnerability to natural hazards. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 3 (three), 2009, 164-173

Roobol, M. J. and Smith, A. L., 2004, Geologic Map of Commonwealth of dominica, Due west Indies: Geology Department, University of Puerto Rico at Mayaguez. Electronic certificate, available at http://world wide web.caribbeanvolcanoes.com/commonwealth of dominica/content/dominicamap.pdf

Schmidt J, Matcham I, Reese South, Male monarch A, Bell R, Henderson R, Smart G, Cousins J, Smith Westward, Heron D, (2011). Quantitative multi-take a chance analysis for natural hazards: a framework for multi-risk modelling. Natural Hazards 58(3), 1169-1192

Schneider PJ, Schauer BA, (2006). HAZUS‚ its development and its future. Natural Hazards Review 7(2), forty-44.

Shi P Shuai J Chen W and Lu L (2010). Report on the risk cess and risk transfer mode of large scale disasters. In The 3rd International Disaster and Take chances Briefing IDRC, Davos, Switzerland

Talbot, J. (2016) What's right with risk matrices?  http://www.jakeman.com.au/media/whats-right-with-chance-matrices

Tarvainen T Jarva J and Greiving S (2006). Spatial pattern of hazards and run a risk interactions in Europe. In Schmidt-Thomé, P. (ed), Natural and Technological Hazards and Risks Affecting the Spatial Development of European Regions, 42: 83–91

Un-ISDR (2009). Terminology on Disaster run a risk Reduction. URL:    https://www.unisdr.org/we/inform/terminology

Van Westen CJ, Castellanos Abella EA and Sekhar LK (2008) Spatial data for landslide susceptibility, hazards and vulnerability cess : an overview. In: Engineering geology, 102 (2008)3-4 pp. 112-131.

Van Westen  C J  Quan Luna  B  Vargas Franco  R D (2010) Development of preparation materials on the use of geo - information for multi - hazard risk assessment in a mountainous surround. In: Mountain risks : bringing science to society : proceedings of the Mountain Risks International Conference, Firenze, Italia, 24-26 November 2010 / ed. by J.-P. Malet, T. Glade and N. Casagli. - Strasbourg : CERG, 2010. ISBN 2-95183317-1-v. pp. 469-475.

Van Westen CJ, Straatsma MW, Turdukulov UD, Feringa WF, Sijmons K, Bakhtadze K, Janelidze T, Kheladze N. (2012) Atlas of natural hazards and risks of Georgia : e-book. Tbilisi, Caucasus Environmental NGO Network (CENN), University of Twente Faculty of Geo-Data and Earth Ascertainment (ITC), 2012. ISBN: 978-9941-0-4310-9.

Van Westen CJ (2013). Remote sensing and GIS for natural hazards assessment and disaster hazard direction. In: Shroder, J. (Editor in Main), Bishop, M.P. (Ed.), Treatise on Geomorphology. Academic Press, San Diego, CA, vol. 3, Remote Sensing and GIScience in Geomorphology, pp. 259–298.

martinezthistil1996.blogspot.com

Source: http://www.charim.net/methodology/55

Share this post