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Emmanouil N Anagnostou

Biographic Data

ID6717840
NAMEEmmanouil N Anagnostou
GIVEN NAMESEmmanouil N
FAMILY NAMEAnagnostou
SIGNATUREANAGNOSTOU E N
AFFILIATIONSUniversity of Connecticut
ORCID0000-0002-1468-9422
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS2
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2011
LATEST PUBLICATION YEAR2021
H-INDEX1
  • Estimation of flood-damaged cropland area using a convolutional neural network

    Open Access•Rehenuma Lazin, Xinyi Shen et al.•ARTICLE•Environmental Research Letters•2021

    Flood damage to croplands poses a significant threat to global food security. Effective disaster management to cope with future climate change, especially extreme precipitation, requires a robust framework to estimate such damage. For this study, we develop a model based on a convolutional neural network to estimate the area (in acres) of cropland damaged by flooding at the county level. Then we demonstrate the model’s performance for the period …

  • Flash flood forecasting, warning and risk management

    Open Access•Marco Borga, Emmanouil N Anagnostou et al.•ARTICLE•Environmental Science & Policy•2011•Cited by: 2

  • Flash flood forecasting, warning and risk management

    Open Access•Marco Borga, Emmanouil N Anagnostou et al.•ARTICLE•Environmental Science & Policy•2011•Cited by: 2

  • Flash flood forecasting, warning and risk management

    Open Access•Marco Borga, Emmanouil N Anagnostou et al.•ARTICLE•Environmental Science & Policy•2011•Cited by: 2

  • Estimation of flood-damaged cropland area using a convolutional neural network

    Open Access•Rehenuma Lazin, Xinyi Shen et al.•ARTICLE•Environmental Research Letters•2021

    Flood damage to croplands poses a significant threat to global food security. Effective disaster management to cope with future climate change, especially extreme precipitation, requires a robust framework to estimate such damage. For this study, we develop a model based on a convolutional neural network to estimate the area (in acres) of cropland damaged by flooding at the county level. Then we demonstrate the model’s performance for the period …

Computer Science (2 works) · Environmental Science (2 works) · Flood myth (2 works) · Flood Risk Assessment and Management (2 works) · Geography (2 works) · Agriculture (1 work) · Agronomy (1 work) · Algorithm (1 work) · Business (1 work) · Climate change (1 work)

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