Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting
Author:
Publisher
Springer Science and Business Media LLC
Subject
Water Science and Technology,Civil and Structural Engineering
Link
http://link.springer.com/article/10.1007/s11269-017-1792-5/fulltext.html
Reference38 articles.
1. Abrahart RJ (2003) Neural network rainfall-runoff forecasting based on continuous resampling. J Hydroinf 5(1):51–61
2. Abrahart RJ, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14:2157–2172
3. Aqil M, Kita I, Yano A, Nishiyama S (2007) A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J Hydrol 337:22–34
4. Bozorg-Haddad O, Zarezadeh-Mehrizi M, Abdi-Dehkordi M, Loaiciga HA, Marini MA (2016) A self-tuning ANN model for simulation and forecasting of surface flows. Water Resour Manag 30(9):2907–2929
5. Brath A, Montanari A, Toth E (2002) Neural networks and non-parametric methods for improving realtime flood forecasting through conceptual hydrological models. Hydrol Earth Syst Sci 6(4):627–640
Cited by 65 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. Comprehensive performance analysis of training functions in flow prediction model using artificial neural network;Water SA;2024-04-30
3. Analysis of dissolved oxygen influencing factors and concentration prediction using input variable selection technique: A hybrid machine learning approach;Journal of Environmental Management;2024-04
4. Integrated forecasting method of medium-and long-term runoff by ridge regression based on optimal sub-model selection;Water Supply;2024-02-24
5. Application of hybridized ANN–GARCH, ANN–SETAR, MARS–SPSO, and CANFIS–SPSO meta-models for improving accuracy of monthly streamflow prediction;Soft Computing;2024-01-05
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