Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model

Deo, Ravinesh C.; Tiwari, Mukesh; Adamowski, Jan; Quilty, John

doi:10.1007/s00477-016-1265-z

Cited by 191 publications

(81 citation statements)

References 138 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since EWT borrows the framework of classic WT in the decomposition and reconstruction processes [27], and three-level decompositions have been considered as the most appropriate in various studies, such as wind speed forecasting [27,28], drought index prediction [38] and daily river flow forecasting [39]; the decomposition level is determined as three in this study. Graphical representations of the decomposed subseries using EWT for four study stations are illustrated in Figure 5.…”

Section: Decomposing and Reconstruction Using Ewtmentioning

confidence: 99%

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Tian

Zhou

Zhang

et al. 2017

Water

101

View full text Add to dashboard Cite

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.

show abstract

Section: Decomposing and Reconstruction Using Ewtmentioning

confidence: 99%

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Tian

Zhou

Zhang

et al. 2017

Water

101

View full text Add to dashboard Cite

show abstract

“…The learning speed of the ELM model is relatively faster than the conventional feed-forward network without the need for too much human intervention, but this model is able to offer better generalization performance than the conventional ANN model [42,46,47,75]. ELM is based on single-layer feed-forward neural networks (SLFNNs) architecture [5] where there are 3 layers, including the input layer, hidden layer, and output layer.…”

Section: Extreme-learning Machinementioning

confidence: 99%

“…Notably, the SVR model is a statistical model based in theory that utilizes the regularization framework, presenting advancement over conventional artificial neural network models; whereas the ELM model is a fast and efficient neuro-computational approach offering an improvement in its design and universal approximation capability compared to conventional ANN models. The ELM model was shown to perform more accurately than the SVR and ANN models for drought studies [46] and the simulation of streamflow [47]. To the authors' best knowledge, the SVR and ELM models have not been fully explored for the future projection of ET 0 .…”

Section: Introductionmentioning

confidence: 98%

Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China

Yin

Feng

Yang

et al. 2017

Water

View full text Add to dashboard Cite

This study aims to project future variability of reference evapotranspiration (ET 0 ) using artificial intelligence methods, constructed with an extreme-learning machine (ELM) and support vector regression (SVR) in a mountainous inland watershed in north-west China. under the RCP4.5 (RCP8.5) scenario. Compared to the historical period, the relative changes in ET 0 were found to be approximately 2%, 5% and 6% (2%, 7% and 13%), during the near, mid-and long-term periods, respectively, under the RCP4.5 (RCP8.5) warming scenarios. In accordance with the analyses, we aver that the opportunity to downscale monthly ET 0 with artificial intelligence is useful in practice for water-management policies.

show abstract

“…Consequently, two parameters are required to be identified by a complicated search process, which are parameters 'c' and 'σ' [63,64]. The latest optimization algorithm GOA is utilized to identify the best values of these parameters.…”

Section: The Lssvm Approachmentioning

confidence: 99%

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Zhao

Guo

2018

Sustainability

View full text Add to dashboard Cite

Abstract:As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge-Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

show abstract

Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model

Cited by 191 publications

References 138 publications

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Contact Info

Product

Resources

About