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

• Published in: The Science of the total environment Vol. 610-611; pp. 1390 - 1399
• Main Authors: Shi, Bin, Wang, Peng, Jiang, Jiping, Liu, Rentao
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2018
• Subjects: Anomaly detection; aquatic environment; Back-propagation neural networks; case studies; chemical oxygen demand; management systems; monitoring; neural networks; Potomac River; prediction; storms; surface water; Surrogate parameters; time series analysis; Virginia; water management; Water quality; watershed management; watersheds; wavelet; Wavelet denoising; Potomac River; Virginia; Back-propagation neural networks; Surrogate parameters; Anomaly detection; Wavelet denoising; Water quality

It is critical for surface water management systems to provide early warnings of abrupt, large variations in water quality, which likely indicate the occurrence of spill incidents. In this study, a combined approach integrating a wavelet artificial neural network (wavelet-ANN) model and high-frequen...