Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks
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Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Diel dissolved oxygen (DO) time series measured continuously using proximal sensors in situ for a temperate lake were denoised using discrete wavelet transform (DWT) with the orthogonal wavelet families of coiflet, daubechies, and symmlet with order of 10. Diel DO time series denoised were modeled using nine temporal artificial neural networks (ANNs) as a function of water level, water temperature, electrical conductivity, pH, day of year, and hour. Our results showed that time-lag recurrent network (TLRN) using denoised data emulated diel DO dynamics better than the best-performing TLRN using the original data, time-delay neural network (TDNN), and recurrent network (RNN). Daubechies basis dealt with diel DO data slightly better than the other bases given its coefficient of determination (r (2) = 87.1 %), while symmlet performed slightly better than the other bases in terms of root mean square error (RMSE = 1.2 ppm) and mean absolute error (MAE = 0.9 ppm).
Açıklama
Anahtar Kelimeler
Diel Dynamics, Discrete Wavelet Transform, Surface Water, Time Series
Kaynak
Environmental Monitoring And Assessment
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
186
Sayı
3
