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In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application...

ABSTRACT One of the key elements in achieving sustainable water resources and environmental management is forecasting the future condition of the surface water resources. In this study, the performance of a river flow forecasting model is improved when different input combinations and signal processing techniques are applied on multi-layer backpropagation neural networks. Haar, Coiflet and Daubechies wavelet analysis are coupled with backpropagation neural networks model to develop hybrid wavelet neural networks models. Different models with different input selections and structures are developed for daily, weekly and monthly river flow forecasting in Ellen Brook River, Western Australia. Comparison of the performance of the hybrid approach with that of the original neural networks indicates that the hybrid models produce significantly better results. The improvement is more substantial for peak values and longer-term forecasting, in which the Nash–Sutcliffe coefficient of efficiency for monthly river flow forecasting is improved from 0.63 to 0.89 in this study. Copyright © 2014 John Wiley & Sons, Ltd.

The need for an accurate river flow forecasting model has grown rapidly in the past decades for achieving better risk-based water resources planning due to issues like water demand increase and climate change. In this paper a hybrid Wavelet-Neural Network model (WNN) is developed to predict daily river flow. WNN is based on combination of wavelet analysis and Artificial Neural Networks (ANNs), which are one of the most reliable recent methods for hydrological time series predictions. 30 years of daily river flow and rainfall data from the Dingo road station on Harvey River, Western Australia are used in this study. Both rainfall and runoff time series are decomposed into multi-frequency time series by using the Harr and Daubechies wavelet No5 (db5), then the wavelet coefficients are imposed as input data to a feed-forward back propagation ANNs. The best structure of ANNs is chosen by trial and error to reach best daily river flow forecasting. Comparing the results with those of the ...

Accurate river flow forecasts play a key role in sustainable water resources and environmental management. Recently, computational intelligence approaches have become increasingly popular due to minimum information requirements and their ability to simulate nonlinear and non-stationary characteristics of hydrological process. In this paper, the performance of seasonal river flow forecasting model is improved when different input combinations and data-preprocessing techniques are applied on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with ANFIS model to develop hybrid wavelet neuro-fuzzy model (WNF). Different models with different input selection and structure are developed for daily river flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The River flow time series is decomposed into multi-frequency time series by discrete wavelet transform (DWT) using the Haar, Coiflet number 1 and Daubechies number 5...