Artificial intelligence applications in hydrological studies and ecological restoration of watersheds: A systematic review DOI Creative Commons
Fernando Morante-Carballo, Mirka Arcentales-Rosado, Jhon Caicedo-Potosí

et al.

Watershed Ecology and the Environment, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Two-stage meta-ensembling machine learning model for enhanced water quality forecasting DOI

Sepideh Heydari,

Mohammad Reza Nikoo,

Ali Mohammadi

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 641, P. 131767 - 131767

Published: Aug. 3, 2024

Language: Английский

Citations

7
Explainable Artificial Intelligence for Reliable Water Demand Forecasting to Increase Trust in Predictions DOI Creative Commons
Claudia Maußner, Martin Oberascher,

Arnold Autengruber

et al.

Water Research, Journal Year: 2024, Volume and Issue: 268, P. 122779 - 122779

Published: Nov. 9, 2024

Language: Английский

Citations

5
Assessment of geothermal waters in Yunnan, China: Distribution, quality and driving factors DOI
Zhaojun Zeng, Yang Li, Yuejü Cui

et al.

Geothermics, Journal Year: 2025, Volume and Issue: 130, P. 103323 - 103323

Published: March 16, 2025

Language: Английский

Citations

0
Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review DOI Creative Commons
Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik

et al.

Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520

Published: May 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Language: Английский

Citations

0
Artificial intelligence applications in hydrological studies and ecological restoration of watersheds: A systematic review DOI Creative Commons
Fernando Morante-Carballo, Mirka Arcentales-Rosado, Jhon Caicedo-Potosí

et al.

Watershed Ecology and the Environment, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0