An analysis of the relation between natural disasters and Big Data

Velev, Dimiter; Златева, Пламена

doi:10.1504/ijds.2016.081372

Cited by 10 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 30,31,[34][35][36]44,50,56,[62][63][64][65][66][67][68][69][70][71][72][73][74][75] Flood inundation depth Identify the hazard/risk level to the community and infrastructure. [36,38,43,64,74] Flood intensity flood frequency/flood magnitude/ return period Predict the hazard levels and use them to evaluate possible damage to the community, infrastructure, and natural environment.…”

Section: Intelligence On Flood Hazardsmentioning

confidence: 99%

“…[30,37,62,70,77,81] Population density/demography and distribution Useful for response planning and relief operations. [32,68,73] Basic needs (food, water, etc.) Acquisition and managing basic needs during the response period.…”

Section: Mobility Of Crowdmentioning

confidence: 99%

“…The spatial distribution and density of populations are primary data that are required in order to identify and estimate an exposed population for a given hazard [32,68,73,93]. Population data are usually obtained from the national census, available in spatially aggregated forms up to local administrative boundaries, which are too coarse for disaster impact analyses.…”

Section: Population Density Distribution and Demographymentioning

confidence: 99%

See 2 more Smart Citations

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Samansiri

Fernando

Ingirige

2022

Water

View full text Add to dashboard Cite

Deaths and property damage from floods have increased drastically in the past two decades due to various reasons such as increased populations, unplanned developments, and climate change. Such losses from floods can be reduced by issuing timely early warnings and through effective response mechanisms based on situational intelligence during emerging flood situations. This paper presents the outcome of a literature review that was conducted to identify the types and sources of the intelligence required for flood warning and response processes as well as the technology solutions that can be used for offering such intelligence. Twenty-seven different types of intelligence are presented together with the technologies that can be used to extract such intelligence. Furthermore, a conceptual architecture that illustrates how relevant technology solutions can be used to extract intelligence at various stages of a flood cycle for decision-making in issuing early warnings and planning responses is presented.

show abstract

Section: Intelligence On Flood Hazardsmentioning

confidence: 99%

Section: Mobility Of Crowdmentioning

confidence: 99%

Section: Population Density Distribution and Demographymentioning

confidence: 99%

See 1 more Smart Citation

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Samansiri

Fernando

Ingirige

2022

Water

View full text Add to dashboard Cite

show abstract

“…Location based information assist the recovery processes [1]. Even social media analysis can be used to help understand and identify the possibility of recruiting volunteers needed for disaster recovery processes [12]. Another big data application can be used in Nepal is the tourism section.…”

Section: Big Data Applications For E-governmentmentioning

confidence: 99%

Big Data Challenges for e-Government Services in Nepal

Pradhan

Shakya

2018

J. Inst. Engineering

View full text Add to dashboard Cite

Abstract:With the digital disruption governments all over the world have envisaged into big data technology with the purpose of seeking values for the benefit of governmental activities. Governments have taken up this technology as a game changer of the e-governance. Citizen centric data over the web access and activities over the smart handsets have driven large volume of data not only in structured form but largely in unstructured form. The necessity of processing and analyzing the exploding data sets has driven governments to adopt this technology. This paper studies the possibility of digitalizing Nepal e-government services with innovative problem solving measures by adapting the Big Data applications. Following the global trend of digitalization and the ICT infrastructure development, Nepal e-Government services can initiate Big Data technology to resolve many of the governmental services. This paper explores some big data applications for the e-government of Nepal and further studies some challenges that Nepal could face while adopting the Big data in the e-government services.

show abstract

“…Meanwhile, we observe that data science [48] can play a vital role during the preparation of evacuation plans that guide people in safe places and reconstruction/recovery plans oriented to social benefits. This role is a direct consequence of combining multiple fields and sources considered by data scientists in their mission of interpreting data for the advantage of decision making.…”

Section: Introductionmentioning

confidence: 99%

Toward Effective Response to Natural Disasters: A Data Science Approach

et al. 2021

View full text Add to dashboard Cite

Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands of living beings. These events are difficult to be overcome both by the populations and by government authorities. Two challenging issues require in particular to be addressed: find an effective way to evacuate people first, and later to rebuild houses and other infrastructures. An adequate recovery strategy to evacuate people and start reconstructing damaged areas on a priority basis can then be a game changer allowing to overcome effectively those terrible circumstances. In this perspective, we here present DiReCT, an approach based on i) a dynamic optimization model designed to timely formulate an evacuation plan of an area struck by an earthquake, and ii) a decision support system, based on double deep Q Network, able to guide efficiently the reconstruction the affected areas. The latter works by considering both the resources available and the needs of the various stakeholders involved (e.g., residents social benefits and political priorities). The ground on which both the above solutions stand was a dedicated geographical data extraction algorithm, called "GisToGraph", especially developed for this purpose. To check applicability of the whole approach, we dovetailed it on the real use-case of the historical city center of L'Aquila (Italy) using detailed GIS data and information on urban land structure and buildings vulnerability. Several simulations were run on the underlining network generated. First, we ran experiments to safely evacuate in the shortest possible time as many people as possible from an endangered area towards a set of safe places. Then, using DDQN, we generated different reconstruction plans and selected the best ones considering both social benefits and political priorities of the building units. The described approaches are comprised in a more general data science framework delved to produce an effective response to natural disasters.

show abstract

An analysis of the relation between natural disasters and Big Data

Cited by 10 publications

References 0 publications

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Big Data Challenges for e-Government Services in Nepal

Toward Effective Response to Natural Disasters: A Data Science Approach

Contact Info

Product

Resources

About