Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature

Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature

Precise river water temperature (WT) forecasts are essential for monitoring water quality. This study addresses the limited use of signal decomposition in hybrid WT prediction models by proposing three methods: namely ensemble empirical mode decomposition (EEMD) on AdaBoost, long short-term memory (...

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Bibliographic Details
Main Authors: Mehdi Gheisari, Jana Shafi, Saeed Kosari, Samaneh Amanabadi, Saeid Mehdizadeh, Christian Fernandez Campusano, Hemn Barzan Abdalla
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
River water temperature
forecasting
hybrid models
deep learning
time horizon
ensemble empirical mode decomposition
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2025.2450477
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https://www.tandfonline.com/doi/10.1080/19942060.2025.2450477

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