Generalizability evaluations of heterogeneous ensembles for river health predictions-Reference-Cited by-同舟云学术

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Generalizability evaluations of heterogeneous ensembles for river health predictions

Published:2024-09 Issue: Volume:82 Page:102719
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Park Taeseung,Shin Jihoon,Park Baekyung,Moon Jeongsuk,Cha YoonKyung

Funder

Ministry of Environment

National Research Foundation of Korea

National Institute of Environmental Research

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference58 articles.

1. A review of uncertainty quantification in deep learning: techniques, applications and challenges;Abdar;Inf. Fusion,2021

2. Predicting student academic performance using multi-model heterogeneous ensemble approach;Adejo;J. Appl. Res. High. Educ.,2018

3. Social benefits of river restoration from ecosystem services perspective: a systematic review;Basak;Environ. Sci. Pol.,2021

4. Reconciling modern machine-learning practice and the classical bias–variance trade-off;Belkin;Proc. Natl. Acad. Sci. USA,2019

5. Anthropogenic stresses on the world’s big rivers;Best;Nat. Geosci.,2019

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1. Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau;Ecological Informatics;2024-09

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