New estimates of flood exposure in developing countries using high-resolution population data
Flood risk modelling neglects the location of people and assets. Here the authors applied machine learning techniques and high-resolution population data to reinvestigate the impact of population distributions on flood exposure and showed that populations are generally represented as risk-averse and...
Guardado en:
Autores principales: | Andrew Smith, Paul D. Bates, Oliver Wing, Christopher Sampson, Niall Quinn, Jeff Neal |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/c14b36755011474ba2a151bf0248d457 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
New insights into US flood vulnerability revealed from flood insurance big data
por: Oliver E. J. Wing, et al.
Publicado: (2020) -
Design flood estimation for global river networks based on machine learning models
por: G. Zhao, et al.
Publicado: (2021) -
Assessing population exposure to coastal flooding due to sea level rise
por: Mathew E. Hauer, et al.
Publicado: (2021) -
New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding
por: Scott A. Kulp, et al.
Publicado: (2019) -
Estimating the population at high risk for tuberculosis through household exposure in high-incidence countries: a model-based analysis
por: Jennifer M. Ross, MD, et al.
Publicado: (2021)