Work

Optimising U-Net models for large-scale rainfall-runoff modelling

Year: 2026

Type: article

Abstract: Background High-resolution flood simulations are critical for risk management, yet conventional numerical solvers remain computationally prohibitive for real-time applications.Methods We propose a deep learning-based super-resolution framework that couples the TRITON hydraulic simulator with a U-Net architecture. The model is trained on paired constant-rainfall simulations using coarse depth maps and fine topography. Sensitivity analyses are conducted on topographic feature selection and domain decomposition strategies to optimize performance.Results Despite being trained on constant-rainfall scenarios, the U-Net model successfully generalizes to realistic, time-varying rainfall events while preserving high-resolution details.

Source: Digital Water

DOI: 10.1080/28375807.2026.2724255

Authors: Juan-Manuel Pérez-García-de-Carellán, Mario Morales-Hernández, Pilar García-Navarro

Institution: Universidad de Zaragoza

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: hybrid

Funders: Ministerio de Ciencia e Innovación, Universidad de Zaragoza, Gobierno de Aragón, Oak Ridge National Laboratory