Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins DOI
Zhenglei Xie, Wei Xu, Bing Zhu

и другие.

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Язык: Английский

AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability DOI
Haoyang Wu, Jing Liu,

Biming Liang

и другие.

Journal of the Knowledge Economy, Год журнала: 2024, Номер unknown

Опубликована: Июнь 13, 2024

Язык: Английский

Процитировано

8
Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets DOI Open Access
F. M. Hasan,

Paul Medley,

Jason Drake

и другие.

Water, Год журнала: 2024, Номер 16(13), С. 1904 - 1904

Опубликована: Июль 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Язык: Английский

Процитировано

8
AI-driven participatory environmental management: Innovations, applications, and future prospects DOI Creative Commons
Márcia R. C. Santos, Luísa Cagica Carvalho

Journal of Environmental Management, Год журнала: 2025, Номер 373, С. 123864 - 123864

Опубликована: Янв. 1, 2025

The rapid advancement of Artificial Intelligence (AI) presents unprecedented opportunities for participatory environmental management. This paper explores the integration AI technologies into approaches, which engage diverse stakeholders in decision-making processes. Using artificial intelligence, a corpus 80 papers was compiled and subsequently analyzed with text mining tools. By identifying systematizing academics' contributions to knowledge about AI-driven tools, this study also discusses challenges ethical considerations inherent deployment, emphasizing need transparent, equitable, accountable systems. Looking ahead, we outline future prospects management, focusing on potential foster adaptive management strategies, enhance stakeholder collaboration, support sustainable development goals.

Язык: Английский

Процитировано

1
Unveiling the Hidden Connections: Using Explainable Artificial Intelligence to Assess Water Quality Criteria in Nine Giant Rivers DOI
Sourav Kundu,

P. K. Datta,

Puja Pal

и другие.

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 144861 - 144861

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins DOI
Zhenglei Xie, Wei Xu, Bing Zhu

и другие.

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Язык: Английский

Процитировано

1