[HTML][HTML] Urban flood modeling using deep-learning approaches in Seoul, South Korea

X Lei, W Chen, M Panahi, F Falah, O Rahmati… - Journal of …, 2021 - Elsevier
Identification of flood-prone sites in urban environments is necessary, but there is insufficient
hydraulic information and time series data on surface runoff. To date, several attempts have …

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms

J He, L Zhang, T ** and flood vulnerability analysis of residential buildings: The case of Khando River in eastern Nepal
S Thapa, A Shrestha, S Lamichhane, R Adhikari… - Journal of Hydrology …, 2020 - Elsevier
Study region This study considers the Khando River (a tributary of Koshi River) in eastern
Nepal. Study focus To quantify the hazard and vulnerabilities across one of the frequently …

Water identification from high-resolution remote sensing images based on multidimensional densely connected convolutional neural networks

G Wang, M Wu, X Wei, H Song - Remote sensing, 2020 - mdpi.com
The accurate acquisition of water information from remote sensing images has become
important in water resources monitoring and protections, and flooding disaster assessment …

Application of entropy weighting method for urban flood hazard map**

H Malekinezhad, M Sepehri, QB Pham, SZ Hosseini… - Acta geophysica, 2021 - Springer
Flooding is one of the most frequently occurring natural hazards worldwide. Map** and
assessment of possible flood hazards are critical components of the evaluation and …

Improving urban flood susceptibility map** using transfer learning

G Zhao, B Pang, Z Xu, L Cui, J Wang, D Zuo… - Journal of Hydrology, 2021 - Elsevier
The flood inventory in urban areas is often difficult to collect and therefore inadequate for
training a machine learning (ML)-based assessment model. In this study, we investigated …