Performance Improvement of Biological BOD in Rivers based on De-noising Comparison Wavelet-ANN Conjunction, GP, ANN and MLR Methods (Case Study:Karaj Dam Outlet Station)

Document Type : Research Article


Civil Engineering Department, University of Qom, Qom, Iran


This study considered artificial neural network (ANN), multi-linear regression (MLR), Genetic
Programming (GP) and wavelet analysis and ANN combination (WANN), models for monthly water
biological oxygen demand (BOD) in station Karaj Dam outlet and investigates the effects of data
preprocessing on model performance using discrete wavelet. For this purpose, In the first proposed
model, observed time series of BOD were decomposed into several subtime series at different scales by
discrete wavelet transform. Then these subtime series were imposed as inputs to the ANN method. In
the second proposed model, observed time series of BOD were decomposed at ten scales by wavelet
analysis. Then, total effective time series BOD were imposed as inputs to the neural network model for
prediction of BOD in one month ahead. Results showed that the wavelet neural network models
performance was better in prediction rather than the neural network and multilinear regression
models. The wavelet analysis model produced reasonable predictions for the extreme values. This
model dropped the mean absolute percentage error for the MLR, GP, ANN and the first hybrid
models from 1.87, 0.91, 0.65 and 0.46 respectively, to 0.44 and increased the Nash-Sutcliffe model
efficiency coefficient from 0.23, 0.53, 0.73 and 0.81 to 0.83.


Main Subjects

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