Impact Statement:The research seeks to contribute to the hydrological modeling area with a surrogate model based on physics-informed neural networks (PINNs) to water flow in a watershed. ...Show More
Abstract:
The impacts incurred by floods regularly affect the planet's population, inflicting social and economic problems. Optimal control strategies based on reservoir management...Show MoreMetadata
Impact Statement:
The research seeks to contribute to the hydrological modeling area with a surrogate model based on physics-informed neural networks (PINNs) to water flow in a watershed. In practice, these models use conceptual or data-driven methods. Conceptual models to reach the precision provided by the methodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system behavior. We introduce PINNs as a viable solution for training neural networks with a few training data and estimating the partial differential equation parameters that govern the underlying dynamics. In addition, we present a novel strategy for training PINNs inspired by Bayesian inference for parameter estimation problems. The research aims to brin...
Abstract:
The impacts incurred by floods regularly affect the planet's population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained from conceptual or data-driven methods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accurac...
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 5, Issue: 3, March 2024)