47 citations · 50 across the 3 of their papers we have counts for
5 papers
An evaluation of deep learning models for predicting water depth evolution in urban floods
Stefania Russo, Nathanaël Perraudin, Steven Stalder +4
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water…
Water level prediction from social media images with a multi-task ranking approach
P. Chaudhary, S. D'Aronco, J. P. Leitao +2
Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and c…
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Zifeng Guo, Joao P. Leitao, Nuno E. Simoes +1
Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding an…
Modeling overland flow from local inflows in almost no-time, using Self Organizing Maps
Joao P. Leitao, Mohamed Zaghloul, Vahid Moosavi
Physically-based overland flow models are computationally demanding, hindering their use for real-time applications. Therefore, the development of fast (and reasonably accurate) ov…
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators
Juan Pablo Carbajal, João Paulo Leitão, Carlo Albert +1
Many model based scientific and engineering methodologies, such as system identification, sensitivity analysis, optimization and control, require a large number of model evaluation…