Sandwiched Lo-Res Simulation for Scalable Flood Modeling

• Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2420 - 2424
• Main Authors: Putra, Refaldi I. D., Ishikawa, Tatsuya, Simumba, Naomi, Tatsubori, Michiaki
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14-04-2024
• Subjects: Accelerated Flood Modelling; Climate Change Impact; Computational modeling; Floods; Laser radar; Machine Learning; Remote Sensing; Sensors; Signal processing; Superresolution; Surfaces
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP48485.2024.10447221

High-resolution flood modeling is enabled by utilizing high-resolution input derived by remote sensing technologies such as Light Detection and Ranging (LiDAR) systems. However, there is a long-standing trade-off between the computational time and spatial resolution for a flood simulation. In this paper, we propose a novel deep learning-based geospatial encoder-decoder for flood modeling consisting of (i) accuracy-preserving coarse-graining of the input topography, (ii) simulating flood with the coarser model, and (iii) downscaling the simulated flood to super-resolution. Our experiments show that our approach accelerates flood simulation up to 50 times faster with 1/16 scale while MSE of 0.0179, which is 10.3% less than the baseline with bilinear interpolation. Especially, we observe 20.5% reduction of MSE on average for the 5% worst cases.