Physics-informed neural networks for solving flow problems modeled by the 2D Shallow Water Equations without labeled data

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131263 - 131263

Published: May 3, 2024

This paper investigates the application of physics-informed neural networks (PINNs) to solve free-surface flow problems governed by 2D shallow water equations (SWEs). Two types PINNs are developed and analyzed: a fully connected network (PIFCN) convolutional (PICN). The eliminate need for labeled data training employing SWEs, initial boundary conditions as components loss function be minimized. Results from set idealized real-world tests showed that prediction accuracy computation time (i.e., time) both may less affected resolution domain discretization when compared against solutions Finite Volume (FV) model. Overall, PICN shows better trade-off between computational speed than PIFCN. Also, our results indicated can provide more 5 times higher FV model, while simulation with coarse (e.g., 10 m) sub-centimeter accurate (RMSE) at least one order magnitude faster PINNs.Results river flood delivered speed-accuracy model in terms predicting depth, models outperformed predictions total discharge.

Language: Английский

Streamflow Simulation Using a Hybrid Approach Combining HEC-HMS and LSTM Model in the Tlawng River Basin of Mizoram, India

Environmental Modeling & Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Language: Английский

Optimum flood inundation mapping in mountainous regions using Sentinel-1 data and a GIS-based multi-criteria approach: a case study of Tlawng river basin, Mizoram, India

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(12)

Published: Nov. 21, 2024

Language: Английский