Vulnerability Assessment - Science topic

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Two examples of data collection and analysis for determining vulnerability indices:

1. https://doi.org/10.1007/s11069-010-9566-x

2. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000290

Vulnerability, though, always must be viewed as a process:

(a) https://www.acme-journal.org/index.php/acme/article/view/866

(b) https://doi.org/10.1007/s11069-016-2294-0

(c) https://doi.org/10.1007/s13753-015-0038-5

(d) https://doi.org/10.1111/area.12457

(e) https://www.whp-journals.co.uk/JPS/article/view/647/482

(f) http://trauma.massey.ac.nz/issues/2011-3/AJDTS_2011-3_03_Lewis.pdf

Some advice from journals at https://authorservices.taylorandfrancis.com/publishing-your-research/choosing-a-journal/journal-metrics/ and

On your topic of household vulnerability assessment, Two examples of data collection and analysis for this topic:

1. https://doi.org/10.1007/s11069-010-9566-x

2. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000290

Vulnerability, though, always must be viewed as a process:

(a) https://www.acme-journal.org/index.php/acme/article/view/866

(b) https://doi.org/10.1007/s11069-016-2294-0

(c) https://doi.org/10.1007/s13753-015-0038-5

(d) https://doi.org/10.1111/area.12457

(e) https://www.whp-journals.co.uk/JPS/article/view/647/482

(f) http://trauma.massey.ac.nz/issues/2011-3/AJDTS_2011-3_03_Lewis.pdf

There is an excellent paper by Madhuri, Tewari and Bhowmick that computes the LVI for Bihar. It provides a detailed methodology.

Livelihood vulnerability index analysis: An approach to study vulnerability in the context of Bihar

Another interesting article is by Gerlitz et al.

Both answers of Steven Cooke and O.S. Abejide are correct. in addition, The reliability is the systematic calculations and prediction of the probability of limit state violation, and Vulnerability assessment is the weakest/ critical point in a system where failure is likely to start first before spreading to other members of the system

Two examples of data collection and analysis for determining a vulnerability index:

1. https://doi.org/10.1007/s11069-010-9566-x

2. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000290

Vulnerability, though, always must be viewed as a process:

(a) https://www.acme-journal.org/index.php/acme/article/view/866

(b) https://doi.org/10.1007/s11069-016-2294-0

(c) https://doi.org/10.1007/s13753-015-0038-5

(d) https://doi.org/10.1111/area.12457

(e) https://jpopsus.org/full_articles/disaster-vulnerability-by-demographics

(f) http://trauma.massey.ac.nz/issues/2011-3/AJDTS_2011-3_03_Lewis.pdf

Data on illiteracy, poverty, and unemployment rates can be searched

Vulnerability to a household/inhabitation may be due to various risk/hazard and other factors and may depend upon time and local geo-environmental setting. It may vary in space and time depending upon various factors, be it anthropogenic changes being rendered to local geo-environment by the residents/local society concerned or be it situations such as extreme events.

We have worked on the technological package of crops (including electrical consumption) in these manuscripts that can serve you:

Completed

Dr. MOHAMMAD FAISAL currently working at the department of computer science and IT, University of Malakand Pakistan, received his M.S. degree in information security management from SZABIST, Pakistan, in 2012, and the Ph.D. degree in network security from the Department of Computer Science and Information Technology, University of Malakand in 2018. His research interests include ML and security of wireless ad hoc networks MANETs, VANETs, IoT, Cloud, Fog, Edge, Blockchain and digital forensics.

There are three dimensions of vulnerability to climate change: exposure, sensitivity, and adaptive capacity.

MATCASC is an open source MATLAB based tool to analyse cascading failures in power grids.

Try this link: https://github.com/trivikverma/MATCASC

Regards

Hi Abdullah Mohammad Raja Alhamdan !

Did you find any dataset or any lead with this problem? I also need same dataset for my project.

Many index-related approaches to social vulnerability display so many weaknesses as to be unhelpful. It might be useful to start my understanding the deep-rooted qualitative nature of vulnerability and where disasters arise from:

1. https://doi.org/10.1016/j.pdisas.2019.100008

2. https://doi.org/10.1007/s13753-018-0188-3

3. https://doi.org/10.1007/s13753-015-0038-5

4. https://doi.org/10.1007/s11069-016-2294-0

5. https://doi.org/10.1007/s13753-015-0046-5

Then, depending on your needs, you could determine the relevance of an index-related approach and how to ensure that you can develop one which truly matches your needs and the traits of social vulnerability.

Dear Leônidas Luiz Volcato Descovi Filho ,

There is no method that can fit every case of study. DRASTIC request several factors that are not available in many countries; the spatial resolution of data is another problem. GOD is made for aquifers that have a large extension; it is not the best solution for small aquifers or where lateral and vertical lithological variations are frequent. SINTACS is the italian little brother of DRASTIS; it has thus the same shortcomings. AVI is based on permeability which is generally not available or the measurement points are poorly distributed...

Since geophysical data are mostly available or surveys could be done, we suggest a geophysical method for countries where data are lacking.

Please check this recent paper:

Regards

Well,

Text book, Groundwater Science / Fitts, 2006.

Regards

Thank you so much .

Dear Sushill,

Hazard = the source of negative incident

Risk = the degree of riskiness (Possibility * Impact)

Vulnerability = the degree of exposure

That's why some authors recommends:

Risk = Possibility * Vulnerability * Impact

Speaking for floods:

Hazard, is the rain

Risk, is the possibility of heavy rain and the impact in case of a flood

Vulnerability, is higher when a city or a mine is near to a river or the drainage system is underdeveloped etc.

In this case a city near a river (all other factors being the same) is more vulnerable to a flood so the Risk is higher.

These titles may have some useful material:

To evaluate recreational resources and develop ecotourism, it is possible to use methods of processing maps, on the basis of which the land zoning of the territory is carried out from the point of view of using it for recreation. A comparative analysis of maps can be made to assess the role of climate change and assess the vulnerability of the territory.

At ITC we focus on using geosptial information for multi-hazard risk assessment (MHRA), including faster and slower onset hazards. I suggest you look at our MHRS guilde book and exercises. There is a project page on https://www.researchgate.net/project/Multi-Hazard-Risk-Assessment

In principle the conceptual approach to vulnerability assessment is the same for the different hazard types.

Best regards, Norman

A good concept is the sustainable livelihoods appraoch, which can be used to easily define indicators and compare communities.

Yes, there are LST products derived from geostationary satellites such as MSG, GOES, MTSAT/Himawari with coarse spatial resolution (4-5 km) that has global coverage. In particular GOES covers North and South America with hourly temporal coverage. If you are looking for Polar satellites then MODIS on board Terra and Aqua provides Day and Night LST (1 km) coverage daily. Less frequent, ASTER LST (90m) and Landsat derived LST (100 m) are some other options. Additionally there are 'Fires and Thermal Anomalies' products derived from Day and Night Suomi NPP/VIIRS (375 m) and MODIS (1 km) that you might be interested in. All relevant links are attached.

Fragility curves is a statistical tool representing the probability of exceeding a given damage state (or performance) as a function of an engineering demand parameter that represents the ground motion (preferably spectral displacement at a given frequency Hence, fragility curves are nothing but finding out the vulnerability of building. It is the graph of peak ground acceleration(PGA on X axis) and % of damage on Y axis.

What is the concept of fragility curve?. Available from: https://www.researchgate.net/post/What_is_the_concept_of_fragility_curve [accessed May 29, 2017]

have a look at my publications. I have some illustrations that will provide an opinion at least.

The method depends on the specific ecological aspect to be assessed. However, a number of researchers are resorting to the use of GIS and remote sensing technics to assess ecological aspects such as biodiversity, fragmentation, invasion etc.

I have found in my own research in Tanzania that often mountain ecosystems can help people cope with drought easier because of the different microclimates around the mountains.  For example, while there may not be pasture in the lower dryland areas during drought, the higher elevations may still provide some pasture for livestock.

High resolution data like LiDAR and digital elevation models can be used to determine degrees of flood risk in stream basins. However, there are many factors that go into predictive flood models, including floodplain width, floodplain depth, floodplain and adjacent lowland vegetation density, geologic constrictions in the floodplain, man-made constrictions such as railroad and road berms and their bridges, size of drainage basin, topography of drainage basin, extent of adjacent lowlands, geomorphology, soil permeability, etc. All of these things attribute to how a flood acts at any given location. It is not just plugging these data into a computer program and getting results. An experienced hydrologist should look at the variables and see/predict how these interactions work to produce flooding using scenarios such as 5, 10, 20, 30, 50, etc. centimeters of rainfall over a given period in different portions of the basin.

Dear Akash,

I would request you to go through the following two papers which may answer your question to some extent:

FEMA 154 talks about RVS for earthquake-affected engineered buildings in the American context. The first paper attempts to extend the idea of RVS propounded by FEMA to non- and semi-engineered buildings in the Indian context.

The second paper takes the idea beyond earthquake-affected engineered buildings to cyclone-affected non-engineered ones.

Best wishes,

Parthasarathi Mukhopadhyay.

Thank you v.much Dr.Annes and Dr.Rajiv Pandey for providing your valuable comments and suggestions.

Dear Alito

The issue may be discussed either only the rural infrastructure or in combination of the bio-physical and socio-ecological drivers,a s suggested by Dr Kalra. The scale may be spatial as per your study. 

You can also look for regression analysis to predict the impacts, as per the selected independent variable.

Regards

Brownly, 

I believe you need to define multiple parameters before you choose the proper tool to asses communities' post-disaster vulnerability.

First, which dimension are you looking at; physical infrastructure, social, economic, or environmental? All of the three dimensions are impacted by disasters, and in fact amplify the disaster impact on the community. 

Moreover, you should also determine if you are looking for vulnerability due to exposure to risk (proximity to hazards) or due to internal/inherent properties of the community. Even though some vulnerability assessment tools account for both in the same model (Environmental Vulnerability Index), I believe it is crucial to define your need. Exposure to risk vulnerability assessment is mostly beneficial for choosing where to rebuild. While inherent vulnerability helps in determining factors that needs to be enhanced to decrease future vulnerability to hazards. 

Furthermore, the level of analysis. Most tools focus on aggregated vulnerability assessment; countries and cities (Economic Resilience Index for OCED countries). However, there are tools that can calculate and assess vulnerability on the block level (Social Vulnerability Index by Cutter et. al 2003). 

Below are a list of some of the tools that I would recommend for such assessment: 

Environmental:

Social: 

Economic: 

If the word danger refers to threat, the equation that relates the threat, risk and vulnerability is:

R = T * V

-R = risk index

-T = threat index

-V = vulnerability index

Therefore, if the vulnerability is very low, the risk will be very low (if the vulnerability or threat is 0 the risk will be 0). If the vulnerability or threat is high, the risk is also high. If the vulnerability is low, and the threat is high or vice versa, the risk will be high or low depending on what values have the threat and vulnerability.

The equation of the disaster is: 

Disaster = threat * vulnerability / social preparation

@William

Thank you for reply.

I am trying to make my question more clear.

I have to analyze effect of groundwater on soil degradation. (Like shallow water table will act excessively by increasing salinity.) I have data of groundwater variation over some years. I have to create a raster surface which will show capacity to act negatively at different locations. For creating such raster, I have to integrate data from many years to a single map.  Is there any method which can be used to integrate/analyze such data.

Dear Mukesh,

The attached papers may be helpful to you.

Best Wishes,

P

Hi Akanni,

Maybe my Book chapter can help you (attached):

Contreras, D., & Kienberger, S. (2012). GIS in the vulnerability assessment and recovery process in a community with elderly and disable people after a disaster. In A. Awotona (Ed.), Rebuilding Sustainable Communities with Vulnerable Populations after the Cameras Have Gone: A worldwide study (pp. 117-154). Cambridge: Cambridge Scholars Publishing, United Kingdom.

 Best regards,

Diana Contreras

Many are now calling it resilience research used in different context - veterans health, mass disasters, children and women's health i nthe context of early trauma, aging, HIV, etc

BS 5628 ‘Code of practice for the use of masonry’

Part 1

‘Structural use of unreinforced masonry’ attempts to align the standard with appropriate BS EN standards. BS 5628-1:2005 was published in December 2005.

Part 2

‘Structural use of reinforced and pre-stressed masonry’. BS 5628-2:2005 was published in December 2005.

Part 3

‘Materials and components, design and workmanship’ incorporates a new version of BS 8000: Part 3: ‘Workmanship on building sites – Code of practice for masonry’ (also available separately). BS 5628-3:2005 was published in December 2005.

Amendments to all three parts of BS 5628 ‘Code of practice for the use of masonry’ brings the suite right up-to-date and maintains the economy and efficiency of masonry design and construction.

You might like to see this book

Design of Masonry Structures,  by A.W. Hendry, B.P. Sinha  and S.R. Davies,  Routledge; 3Rev Ed edition 

You can use ecological modeling such as MaxExt

Check attached paper and if you need more help let me know.

Do not forgot that you have to consider wave height close and along to the shoreline Consequently wave height at the breaking point is probably the best indicator. You may use a numerical model or empirical rules from Goda and others  (see en SPM, 1984)

What you are looking for is very specific to a particular product . There is no way but to build it . You could identify an open source project like Firefox, OpenSSL, Linux Kernel etc for which the bugs (and vulnerabilities) are tracked publicly on their bug tracking systems. So you can pull the locations and build models around that. It is very specific to the project and may not be generic. 

We did similar work some time back not specifically on vulnerabilities but bugs in general , feel free to check out here . https://ece.uwaterloo.ca/~lintan/publications/imbalanced-icse15.pdf

Check this interesting paper in similar lines

The references provided above are good enough to give you a guide. However, the major determinant of using DRASTIC or any Vulnerability assessment method is the parameters. How many of these parameters do you have available to you based on your area of investigation? DRASTIC is not adaptable in all situation and environments. Based on what data you have you could develop your own method. There are very simplified forms of vulnerability assessment that do work out well in certain environemtn, even though they tend to be old-fashioned. I will suggest you have a read of available methods and compare based on what you have and need?

All the best!

Hi

Take a look to my paper entitled: ''Modelling nonpoint source pollution by nitrate of soil in the Mateur plain, northeast of Tunisia". 

There are no reliable data about disasters, as all sources have both (avoidable and unavoidable) technical problems, scope, and not least self interest in manipulating the information (political, financial or otherwise). In addition, you have serious problem of aggregation across events, countries and over time when, especially but not only, using panel data. As long as you are aware of these deep failures, you can entertain some loose thought experiments. But my recommendation would be that you should be extremely careful from reading, let alone interpreting, too much from available data. Have a look at my 2013 book (Routledge) “Disasters and the Networked Economy” (especially Chapter 1: The Problem with Quantitative Studies).

We study the feasibility of the project and identify the elements using ArcGIS for developing the DTM and the river system, MODFLOW® for hydrogeological study and COMSOL® for modeling containment. (Fluid-structure interaction)

This is the only work in the laboratory in this field.

Hi,

I am expert in the application of machine learning methods in geoscience, but not in  aquifer vulnerability. 

Are you looking for something like that?

Bruno Soares et al. (2012): Conceptual elements of climate change vulnerability assessments: a review. International Journal of Climate Change Strategies and Management. Vol. 4 No. 1, pp. 6-35.

Füssel (2007): Vulnerability: A generally applicable conceptual framework for climate change research. Global Environmental Change 17, 155–167.

Here are two papers that may be relevant. They are a bit dated, but well cited. I would check the papers that cite them.

Kaufmann, R.K., K.C. Seto, A. Schneider, L. Zhou and Z. Liu, 2007, Climate response to rapid urban growth: evidence of a human-induced precipitation deficit, Journal of Climate, 20:2299-2306.

Zhou, L. R.E. Dickinson, Y. Tian, J. Fang, R.K Kaufmann, and R.B. Myneni, Evidence for a significant urbanization effect on climate in China, Proceeding National Academy of Sciences, 2004, 101(26):9540-9541.