An Innovative Approach for Calibrating Hydrological Surrogate Deep Learning Models

An Innovative Approach for Calibrating Hydrological Surrogate Deep Learning Models

Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture de...

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Bibliographic Details
Main Authors: Amir Aieb, Antonio Liotta, Alexander Jacob, Iacopo Federico Ferrario, Muhammad Azfar Yaqub
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Remote Sensing
Subjects:
hydrological modeling
feature fusion
surrogate deep learning
parameters calibration
unsupervised catchment regionalization
mountainous region
Online Access:https://www.mdpi.com/2072-4292/17/11/1916
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https://www.mdpi.com/2072-4292/17/11/1916

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