Annual Disaster Statistical Review 2009The numbers and trends

By Femke Vos, J. Rodriguez-Llanes, R. Below, D. Guha-Sapir. 2010

1,165 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/efadde5fabf22151b19cda69ff618187eb1cafbc

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Citations in the corpus

Early fire detection using convolutional neural networks during surveillance for effective disaster management (2017, background): “According to [7] , wildﬁre disasters alone in the year 2015 resulted in 494,0 0 0 victims and caused damage worth US$ 3.1 billion.”
Assessing Stability and Dynamics in Flood Risk Governance (2014, background): “In Europe, flooding, together with landslides, is the most frequently experienced natural hazard, and accounts for the largest number of casualties and most economic damage (Guha-Sapir et al. 2013).”
The United Nations World Water Development Report 2016: Water and Jobs (2016, background): “Worldwide flood damage amounted to over US$50 billion in 2013 and is increasing (Guha-Sapir et al., 2014).”
Multi-Model Projections of River Flood Risk in Europe under Global Warming (2018, background): “For population, it must be noted that observed data come only from the EM-DAT database and that the evaluation of population affected is more complex and prone to errors due to different standards for reporting the number of people hit by floods [58].”
On the unique features of post‐disaster humanitarian logistics (2012, background): “…of U.S. Foreign Disaster Assistance and the Center for Research on the Epidemiology of Disasters, in 2010 more than 297,000 people were killed and over 217 million were affected by natural disasters, and the economic damage has been estimated at over US$123.9 billion (Guha-Sapir et al., 2011).”
On the relation between weather-related disaster impacts, vulnerability and climate change (2014, methodology): “This database is open source and maintained by the World Health Organization (WHO) and the Centre for Research on the Epidemiology of Disasters (CRED) at the University of Louvain, Belgium (Guha-Sapir et al. 2012).”
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, background): “Guha-Sapir et al. (2014) reported that India is one of the most affected countries in Asia by landslides after China.”
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications (2019, unknown): “According to an annual disaster report [11], fire disasters alone affected 494 000 people and resulted in a loss of $3.”
Changes in Wood Anatomy in Tree Rings of Pinus pinaster Ait. Following Wounding by Flash Floods (2010, background): “Floods and the associated overflowing of fluvial or torrential channels as well as the water accumulation in endoreic areas represent a hazard with great associated socio-economic losses worldwide (Scheuren et al. 2008).”