Wavelet transform and neural network model for streamflow forecasting | [Journal of Theoretical and Applied Information Technology • 2022]

Author(s):

1. SALIMEH MALEKPOURHEYDARI: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

2. TEH NORANIS MOHD ARIS: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia,43400 UPM Serdang, Selangor,Malaysia

3. RAZALI YAKOOB: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia,43400 UPM Serdang, Selangor,Malaysia

4. HAZLINA HAMDAN: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia,43400 UPM Serdang, Selangor,Malaysia

Abstract:

Analysis and fast streamflow forecasting are essential. Reliable predicting for river flow, as per the major source of usable water, which can be a crucial factor in the drought analysis and construction of waterrelated infrastructures. Data-driven and hybrid methods are increasingly being used to address the nonlinear and variable components of hydraulic processes. In this paper, a streamflow forecasting model is built utilizing Neural Network (NN) and Wavelet Transform (WT) at Western Australia for Ellen Brook River with the application of Railway Parade station. Initially, the sequences of signals are applying to the wavelet to be evaluated at several levels and extract a sequence of different features from the chosen output in the wavelet. Then, the obtained output is presented to the neural network for tuning to get the best intermittent streamflow forecasting. The existing input and structures are designed for streamflow forecasting. The proposed model has a better performance compared to the previous models. The proposed model is beneficial for application of forecasts to examine the relation between the characteristics of river flow, optimal decomposition degree, data duration, and the precise wavelet transform form.

Page(s): 5419-5428

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 19, Year: 2022

Keywords:

Neural Network NN , Streamflow forecasting , Wavelet Transform WT

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