Wavelet neural network model for reservoir inflow prediction

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean

“…Recently, wavelet transform, which is a data-preprocessing technique, has shown excellent performance in hydrological modelling due to its ability to analyze a signal in both time and frequency (Okkan, 2012). This approach overcomes the basic drawbacks of conventional Fourier transform.…”

“…Nourani et al (2009), showed that the wavelet transform provided effective decompositions of time series so that decomposed data increased the performance of hydrological prediction model by capturing useful information on different resolution level. Hence a wavelet neural network model which uses multi-scale signals as input data can present more suitable prediction performance rather than a single pattern input (Alizadeh et al, 2015;Nourani et al, 2009;Okkan, 2012;Rajaee et al, 2010). Generally, using soft computing techniques such as ANN, ANFIS and WNN has the potential to reduce the computation time and effort and the possibility of errors in the calculation.…”

A Preliminary Study on a Hybrid Wavelet Neural Network Model for Forecasting Monthly Rainfall

“…For instance, prediction can be used to anticipate stock market prices, climatic conditions, soil moisture level, robots positioning and tracking (Chouikhi, N., et al 2017), especially time series forecasting. Over the past few years, artificial intelligence techniques have been frequently used to predict the nonlinear time series and achieved good results (Kisi, O., 2008;Nourani, V.et al 2011) Recently, a wavelet neural network model which uses multi-scale signals as input data that can present more suitable prediction performance rather than a single pattern input (Alizdeh, M.J.et al2015;Nourani, V. et al2009;Okkan, U., 2012;Rajaee, T. et al2010). Generally, using soft computing techniques such as artificial neural networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and wavelet neural network (WNN) has the potential to reduce the computation time and effort and the possibility of errors in the calculation.…”

Development of group method of data handling based on genetic algorithm to predict incipient motion in rigid rectangular storm water channel

“…Therefore, utilizing an equation, which considers the sediment speci cations and hydraulics of the channel when designing an urban drainage system may result in better designs. Due to the availability and the capacity of soft computing in solving complex problems, its methods have been widely applied by di erent disciplines such as hydrology, hydraulic, and sediment transport [12][13][14][15][16][17][18][19][20][21]. Group Method of Data Handling network is one of the self-organized methods amongst soft computing methods based on arti cial intelligence, capable of solving di erent problems in extremely complex nonlinear systems [22][23][24][25].…”