DOI: 10.1016/j.marpolbul.2015.06.052
Scopus记录号: 2-s2.0-84941317757
论文题名: Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean
作者: Alizadeh M.J. ; Kavianpour M.R.
刊名: Marine Pollution Bulletin
ISSN: 0025-326X
EISSN: 1879-3363
出版年: 2015
卷: 98, 期: 2018-01-02 起始页码: 171
结束页码: 178
语种: 英语
英文关键词: Daily prediction
; Neural networks
; Ocean parameters
; Water quality
; Wavelet transform
Scopus关键词: Forecasting
; Neural networks
; Water quality
; Wavelet transforms
; Accurate modeling
; Input variables
; Missing data
; Ocean parameters
; Ocean water quality
; Pacific ocean
; Water quality parameters
; Wavelet neural networks
; Oceanography
; dissolved oxygen
; water
; accuracy assessment
; artificial neural network
; dissolved oxygen
; numerical model
; pollution monitoring
; prediction
; salinity
; temperature
; water quality
; wavelet analysis
; Article
; artificial neural network
; controlled study
; ecosystem monitoring
; intermethod comparison
; machine learning
; measurement accuracy
; Pacific Ocean
; prediction
; salinity
; water analysis
; water quality
; water temperature
; wavelet neural network
; bay
; environmental monitoring
; Hawaii
; procedures
; temperature
; theoretical model
; Hawaii [(ISL) Hawaiian Islands]
; Hawaii [United States]
; Hawaiian Islands
; Hilo Bay
; Bays
; Environmental Monitoring
; Hawaii
; Models, Theoretical
; Neural Networks (Computer)
; Pacific Ocean
; Salinity
; Temperature
; Water Quality
Scopus学科分类: Agricultural and Biological Sciences: Aquatic Science
; Earth and Planetary Sciences: Oceanography
; Environmental Science: Pollution
英文摘要: The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/85970
Appears in Collections: 过去全球变化的重建 全球变化的国际研究计划
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作者单位: Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran
Recommended Citation:
Alizadeh M.J.,Kavianpour M.R.. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean[J]. Marine Pollution Bulletin,2015-01-01,98(2018-01-02)