Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Abstract

Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed to eliminate the redundant noises from the original streamflow series. Secondly, the partial autocorrelation function (PACF) values are explored to identify the inputs for the artificial neural network (ANN) models. Thirdly, the weights and biases of the ANN architecture are tuned and optimized by the multi-verse optimizer (MVO) algorithm. Finally, the simulated streamflow is obtained using the well-trained MVO-ANN model. The proposed hybrid model has been applied to annual streamflow observations from four hydrological stations in the upper reaches of the Yangtze River, China. Parallel experiments using non-denoising models, the back propagation neural network (BPNN) and the ANN optimized by the particle swarm optimization algorithm (PSO-ANN) have been designed and conducted to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can capture the nonlinear characteristics of the streamflow time series and thus provides more accurate forecasting results.

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

“…However, DDM, which are basically numerical and based on biological neuron systems, recently known as an artificial brain or intelligence, have received more attention in water related applications because of their ease, fast progress time, and less data necessity. The ANN-or data-driven models have become increasingly popular in hydrologic forecasting because they are effective at dealing with the nonlinear characteristics of hydrological data [16]. Among the various machine learning methods, artificial neural networks (ANNs), which include back-propagation neural network (BPNN), radial basis function (RBF) neural network, generalized regression neural network (GRNN), Elman neural network, and multilayer feed-forward (MLFF) network, are among the most popular techniques for hydrological time series forecasting [17].…”

Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin

“…ANN, as a postprocessing method, can determine the complex relationships between the inputs and outputs. ANN has been widely used in the hydrology and modeling of water resource systems [5,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

“…ANN is more practical than other techniques due its ability to handle complex nonlinear systems. In previous studies, ANN was applied for various purposes such as precipitation estimation [13][14][15], hydrological modeling [16][17][18][19], hydrometeorological studies [20,21], flood forecasting [22][23][24] and flood inundation [25,26]. Three new hybrid artificial intelligence optimization models (adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms) were presented for flood susceptibility mapping in Iran.…”

High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events

“…Several studies used Multi-Layer Neural Networks, Support Vector Regression, Self-organizing Maps or other nonlinear models for discharge forecasting [37][38][39][40][41]. However, Random Forests in river flow related application were used for water level prediction [42], identification of important variables for flood prediction [43] and sediments concentration [44].…”