Deep Learning Emulators for Large-Scale, High-Resolution Urban Pluvial Flood Prediction

Dinesh Krishna Natarajan; Marco Stricker; Rushan Mukherjee; Marcela Charfuelàn; Marlon Nuske; Andreas Dengel

doi:10.1007/978-3-032-02813-6_17

ScienceGate Book Chapters

BOOK-CHAPTER

Deep Learning Emulators for Large-Scale, High-Resolution Urban Pluvial Flood Prediction

Dinesh Krishna Natarajan Marco Stricker Rushan Mukherjee Marcela Charfuelàn Marlon Nuske Andreas Dengel

Year: 2025 Lecture notes in computer science Pages: 228-235 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-032-02813-6_17

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Pluvial Flood myth Scale (ratio) Deep learning Terabyte Artificial intelligence Remote sensing Cartography Geology Geography Oceanography Archaeology Operating system

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.53

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Flood Risk Assessment and Management

Physical Sciences → Environmental Science → Global and Planetary Change

Meteorological Phenomena and Simulations

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Hydrological Forecasting Using AI

Physical Sciences → Environmental Science → Environmental Engineering

Deep Learning Emulators for Large-Scale, High-Resolution Urban Pluvial Flood Prediction

Metrics

Topics

Related Documents

Improving the Generalizability of Urban Pluvial Flood Emulators by Contextualizing High-Resolution Patches

Improving pluvial flood mapping resolution of large coarse models with deep learning

Improving pluvial flood mapping resolution of large coarse models with deep learning

Improving pluvial flood mapping resolution of large coarse models with deep learning

Urban Pluvial Flood Prediction Using Machine Learning Models