Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble

Published:2021-02-04 Issue:5 Volume:35 Page:1051-1067
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Althoff Daniel^ORCID,Rodrigues Lineu Neiva,Bazame Helizani Couto

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-021-01980-8.pdf

Reference62 articles.

1. Althoff D, Dias SHB, Filgueiras R, Rodrigues LN (2020a) ETo-Brazil: a daily gridded reference evapotranspiration data set for Brazil—repository. Mendeley Data V3. https://doi.org/10.17632/sstjw74ryh.3

2. Althoff D, Dias SHB, Filgueiras R, Rodrigues LN (2020b) ETo-Brazil: a daily gridded reference evapotranspiration data set for Brazil (2000–2018). Water Resour Res. https://doi.org/10.1029/2020WR027562

3. Alvares CA, Stape JL, Sentelhas PC et al (2013) Köppen’s climate classification map for Brazil. Meteorol Z 22:711–728. https://doi.org/10.1127/0941-2948/2013/0507

4. Antonopoulos VZ, Gianniou SK, Antonopoulos AV (2016) Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece. Hydrol Sci J 61:2590–2599. https://doi.org/10.1080/02626667.2016.1142667

5. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115–123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing probabilistic hydrological predictions with mixture density Networks: Accounting for heteroscedasticity and Non-Gaussianity;Journal of Hydrology;2024-09

2. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging;Journal of Hydrology;2024-09

3. Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain;Earth Science Informatics;2024-08-23

4. Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model;Water Resources Research;2024-06-27

5. Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting;Water Resources Research;2024-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3