A wavelet neural network approach to predict daily river discharge using meteorological data

Ömer Gürsoy,Seref Naci Engin

doi:10.1177/0020294019827972

Abstract

This paper reports some part of modelling and data analysis work carried out within the frame of a comprehensive project on the web-based development of watershed information system. This work basically aims to present the daily discharge predictions from the actual discharge along with the meteorological data using a wavelet neural network approach, which combines two methods: discrete wavelet transform and artificial neural networks. The wavelet–artificial neural network model developed provides a good fit with the measured data, in particular with zero discharge in the summer months and also with the peaks and sudden changes in discharge on the test data collected throughout the year. The results indicate that the wavelet–artificial neural network model based predictions are distinctly superior to that of conventional artificial neural network model that corresponds up to an 80% reduction in the mean-squared error between the artificial neural network model and measured data.

Highlights

In literature, integrating artificial neural network (ANN) with wavelet transform in order to reinforce its modelling performance is not a very recent approach
The streamflow data are decomposed to its components using wavelet transform; subsequently, these components are used in an ANN model as inputs
wavelet neural networks (WNNs) model is developed by combining the wavelet transform and ANN model

Summary

Introduction

In literature, integrating artificial neural network (ANN) with wavelet transform in order to reinforce its modelling performance is not a very recent approach. One of the early studies by the author is on fatigue failure assessment of rotating machinery,[1] where vibration data of a critical component were analysed for early fault detection and diagnostics Another early study using wavelet neural networks (WNNs) is on hydrology carried out by Coulibaly et al.[2] In their study, a recursive neural network was used in order to forecast annual discharge. The results indicated that WANN estimations were considerably superior to the conventional ANN In another notable study, Kim and Valdes proposed a similar approach to forecast drought in Mexico using meteorological data.[5] The data were decomposed by means of discrete wavelet transform.

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