Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China

Published:2022-08-30 Issue:3 Volume:30 Page:7498-7509
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Li Qiang,Yang Yinqun,Yang Ling,Wang Yonggui

Funder

National Natural Science Foundation of China

the Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area

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/s11356-022-22758-7.pdf

Reference57 articles.

1. Albanese D, Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C (2013) Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. Bioinformatics 29:407–408. https://doi.org/10.1093/bioinformatics/bts707

2. Antanasijević D, Pocajt V, Povrenović D, Perić-Grujić A, Ristić M (2013) Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environ Sci Pollut Res 20:9006–9013. https://doi.org/10.1007/s11356-013-1876-6

3. Babel MS, Badgujar GB, Shinde VR (2015) Using the mutual information technique to select explanatory variables in artificial neural networks for rainfall forecasting. Meteorol Appl 22:610–616. https://doi.org/10.1002/met.1495

4. Bao Z, Zhang J, Wang G, Fu G, He R, Yan X, Jin J, Liu Y, Zhang A (2012) Attribution for decreasing streamflow of the Haihe River basin, northern China: climate variability or human activities? J Hydrol 460–461:117–129. https://doi.org/10.1016/j.jhydrol.2012.06.054

5. Bennett ND, Croke BFW, Guariso G, Guillaume JHA, Hamilton SH, Jakeman AJ, Marsili-Libelli S, Newham LTH, Norton JP, Perrin C, Pierce SA, Robson B, Seppelt R, Voinov AA, Fath BD, Andreassian V (2013) Characterising performance of environmental models. Environ Model Softw 40:1–20. https://doi.org/10.1016/j.envsoft.2012.09.011

Cited by 23 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. AI-driven modelling approaches for predicting oxygen levels in aquatic environments;Journal of Water Process Engineering;2024-09

2. Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments;Environmental Science and Pollution Research;2024-07-24

3. Predicting water quality in municipal water management systems using a hybrid deep learning model;Engineering Applications of Artificial Intelligence;2024-07

4. Advances in dissolved oxygen prediction and control methods in aquaculture: a review;at - Automatisierungstechnik;2024-06-01

5. Research on out-of-sample prediction method of water quality parameters based on dual-attention mechanism;Environmental Modelling & Software;2024-05

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