Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies

Science of the Total Environment

Volumn 610-611, Issue , 2018, Pages 1390-1399

  Shi, Bin ^a   Wang, Peng ^a   Jiang, Jiping ^a,b   Liu, Rentao ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b SOUTHERN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Anomaly detection; Back propagation neural networks; Surrogate parameters; Water quality; Wavelet denoising

Indexed keywords

BACKPROPAGATION; FINANCIAL DATA PROCESSING; NEURAL NETWORKS; SOIL CONSERVATION; SURFACE WATERS; TIME SERIES; WATER CONSERVATION; WATER MANAGEMENT; WATER QUALITY; WAVELET DECOMPOSITION;

ANOMALY DETECTION; ANOMALY IDENTIFICATION; BACK PROPAGATION NEURAL NETWORKS; HIGH FREQUENCY TIME SERIES; HIGH-FREQUENCY SURFACES; SURROGATE PARAMETERS; WATER QUALITY VARIATIONS; WAVELET DENOISING;

PRODUCED WATER;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; EARLY WARNING SYSTEM; PARAMETERIZATION; PERFORMANCE ASSESSMENT; RIVER WATER; SURFACE WATER; WATER QUALITY; WATERSHED; WAVELET ANALYSIS;

POTOMAC BASIN; UNITED STATES; VIRGINIA;

EID: 85028316154     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2017.08.232     Document Type: Article

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