Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years DOI
Ujjwal Singh, Petr Máca, Martin Hanel

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 97, P. 101807 - 101807

Published: April 16, 2023

Language: Английский

Advanced stepwise machine learning integration of near-real-time precipitation products in China's flood-prone basins DOI
Lingxue Liu,

Huajin Lei

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108197 - 108197

Published: May 1, 2025

Language: Английский

Citations

0
Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on a novel probabilistic double machine learning method DOI
Shaojie Zhan, Aizhong Ye, Lingyun Wu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133484 - 133484

Published: May 1, 2025

Language: Английский

Citations

0
Improving near-real-time satellite precipitation products through multistage modified schemes DOI
Chengcheng Meng, Xingguo Mo, Suxia Liu

et al.

Atmospheric Research, Journal Year: 2023, Volume and Issue: 292, P. 106875 - 106875

Published: June 15, 2023

Language: Английский

Citations

9
Machine learning approaches for reconstructing gridded precipitation based on multiple source products DOI Creative Commons
Giang V. Nguyen, Xuan-Hien Le, Linh Nguyen Van

et al.

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 48, P. 101475 - 101475

Published: July 14, 2023

South Korea is situated in the northeastern region of Asia Recent technological developments have enabled multi-source precipitation products (MSPs), including satellite-based and model-based, to be useful data sources for quantifying spatiotemporal variations precipitation. Unfortunately, main limitation MSPs potential applications inheritance errors with high uncertainty. To tackle this problem, capabilities six machine learning algorithms (Ridge Linear Regression, k-Nearest Neighbors, Support Vector Gradient Boosting Decision Tree, Light Machine, Random Forest) produce new product by merging ground-based investigated. Ground-based from 2003 2017 were utilized train valid process. The robustness ML was highlighted using several evaluation metrics such as continuous indices (modified Kling-Gupta Efficiency root mean square error) categorical (probability detection, false alarm rate, critical success index). results indicate that (1) approaches can merge observed accurately estimate rainfall, particularly basins sparsely distributed rain gauge stations. (2) merged generated showed significant agreement accuracy observation considering rainfall intensity estimation improved capability detecting non-rain events over Korea.

Language: Английский

Citations

9
Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years DOI
Ujjwal Singh, Petr Máca, Martin Hanel

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 97, P. 101807 - 101807

Published: April 16, 2023

Language: Английский

Citations

8