[HTML][HTML] A systematic review of trustworthy artificial intelligence applications in natural disasters

AS Albahri, YL Khaleel, MA Habeeb, RD Ismael… - Computers and …, 2024‏ - Elsevier
Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …

Applications of artificial intelligence for disaster management

W Sun, P Bocchini, BD Davison - Natural Hazards, 2020‏ - Springer
Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

Deep learning methods for flood map**: a review of existing applications and future research directions

R Bentivoglio, E Isufi, SN Jonkman… - Hydrology and Earth …, 2022‏ - hess.copernicus.org
Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

A review of remote sensing applications for water security: Quantity, quality, and extremes

I Chawla, L Karthikeyan, AK Mishra - Journal of Hydrology, 2020‏ - Elsevier
Water resources are critical to the sustainability of life on Earth. With a growing population
and climate change, it is imperative to assess the security of these resources. Over the past …

Recent advances and new frontiers in riverine and coastal flood modeling

K Jafarzadegan, H Moradkhani… - Reviews of …, 2023‏ - Wiley Online Library
Over the past decades, the scientific community has made significant efforts to simulate
flooding conditions using a variety of complex physically based models. Despite all …

Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Y Liao, Z Wang, X Chen, C Lai - Journal of Hydrology, 2023‏ - Elsevier
Urban pluvial floods induced by rainstorms can cause severe losses to human lives and
property. Fast and accurate simulation and prediction of urban pluvial flood are of …

A deep convolutional neural network model for rapid prediction of fluvial flood inundation

S Kabir, S Patidar, X **a, Q Liang, J Neal, G Pender - Journal of Hydrology, 2020‏ - Elsevier
Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still computationally
too demanding for real-time applications. In this paper, an innovative modelling approach …

Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis

LC Chang, JY Liou, FJ Chang - Journal of Hydrology, 2022‏ - Elsevier
The frequency and severity of floods have noticeably increased worldwide in the last
decades due to climate change and urbanization. This study aims to build an urban flood …

Investigating the role of model structure and surface roughness in generating flood inundation extents using one‐and two‐dimensional hydraulic models

Z Liu, V Merwade… - Journal of Flood Risk …, 2019‏ - Wiley Online Library
Hydraulic models play an important role in determining flood inundation areas. When
considering a wide array of one‐(1D) and two‐dimensional (2D) hydraulic models, selecting …

Training machine learning surrogate models from a high‐fidelity physics‐based model: Application for real‐time street‐scale flood prediction in an urban coastal …

FT Zahura, JL Goodall, JM Sadler… - Water Resources …, 2020‏ - Wiley Online Library
Mitigating the adverse impacts caused by increasing flood risks in urban coastal
communities requires effective flood prediction for prompt action. Typically, physics‐based 1 …