Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-022-21597-w.pdf
Reference32 articles.
1. An L, Hao Y, Yeh T-CJ et al (2020) Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks. J Hydrol 589:125320. https://doi.org/10.1016/j.jhydrol.2020.125320
2. Bakalowicz M (2005) Karst groundwater: a challenge for new resources. Hydrogeol J 13:148–160. https://doi.org/10.1007/s10040-004-0402-9
3. Barzegar R, Aalami MT, Adamowski J (2021) Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting. J Hydrol 598:126196. https://doi.org/10.1016/j.jhydrol.2021.126196
4. Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525. https://doi.org/10.1109/ACCESS.2014.2325029
5. Chen X, Zhang X, Church JA et al (2017) The increasing rate of global mean sea-level rise during 1993–2014. Nature Clim Change 7:492–495. https://doi.org/10.1038/nclimate3325
Cited by 12 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. Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework;Journal of Hydrologic Engineering;2024-10
2. A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting;Journal of Hydrology;2024-05
3. Comparative Performance Assessment of Physical-Based and Data-Driven Machine-Learning Models for Simulating Streamflow: A Case Study in Three Catchments across the US;Journal of Hydrologic Engineering;2024-04
4. Evaluating quasi-static and fatigue performance of IN718 gyroid lattice structures fabricated via LPBF: Exploring relative densities;International Journal of Fatigue;2024-01
5. Linear and nonlinear ensemble deep learning models for karst spring discharge forecasting;Journal of Hydrology;2023-12
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