scholarly journals New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine

2017 ◽  
Vol 49 (3) ◽  
pp. 939-953 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi
Keyword(s):  
Extreme Learning Machine ◽  
Polynomial Regression ◽  
Evaluation Criteria ◽  
Computing Methods ◽  
Watershed Hydrology ◽  
Streamflow Forecasting ◽  
Daily Streamflow ◽  
Evolutionary Polynomial Regression ◽  
Learning Machine ◽  
New Formulation

Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.

scholarly journals Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Taiyong Li ◽  
Zijie Qian ◽  
Ting He
Keyword(s):  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Evaluation Criteria ◽  
Ensemble Empirical Mode Decomposition ◽  
Short Term ◽  
Energy Forecasting ◽  
Multiple Kernel ◽  
Novel Approach ◽  
Short Term Load Forecasting ◽  
Learning Machine

Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.

scholarly journals Streamflow Prediction Improvement using Deterministic and Stochastic Wavelet-based Methods Coupled with Extreme Learning Machine

2021 ◽  
Author(s):  
Jiayu Hu ◽  
Bingjun Liu
Keyword(s):  
Extreme Learning Machine ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
East River ◽  
Comparison Results ◽  
Monthly Streamflow ◽  
Probability Prediction ◽  
Stochastic Forecasting ◽  
Learning Machine ◽  
The One

Abstract Accurate and reliable streamflow forecasting is important in hydrology and water resources planning and management. In the present work, wavelet-based direct (DF) and multi-component (MF) forecast methods performed by the à trous algorithm (AT) are proposed for both deterministic and stochastic monthly streamflow prediction improvement. They are developed in the case of the one-month lead streamflow prediction of the East River basin in China, and then compared with the benchmarks that are implemented without wavelet transform so as to evaluate the effectiveness for forecasting accuracy improvement. An existing blueprint that is flexible and practical to incorporate various sources of forecast uncertainty is extended to generate the stochastic probability prediction of streamflow. Partial mutual information is adopted for predictors selection, and six kinds of Extreme learning machine (i.e. one linear ELM and five common nonlinear kinds) are separately used as the learning algorithms coupled with the wavelet-based forecast methods to conduct a comprehensive performance evaluation. The comparison results indicate that both DF and MF can effectively increase the point prediction accuracy of monthly streamflow under deterministic and stochastic forecasting conditions, while MF performs better than DF. For stochastic prediction, it is much more reasonable to consider both parameter and model error uncertainties than just to consider only parameter uncertainty, and with the reasonable setting MF method can significantly improve the probabilistic interval prediction by greatly improving the forecast sharpness. It can be concluded that the approach using AT wavelet-based DF or MF could provide a feasible way for streamflow prediction improvement.

scholarly journals Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine

2021 ◽  
Vol 18 (6) ◽  
pp. 8096-8122
Author(s):  
Hongli Niu ◽  
◽  
Yazhi Zhao
Keyword(s):  
Crude Oil ◽  
Extreme Learning Machine ◽  
Evaluation Criteria ◽  
Oil Price ◽  
Crude Oil Price ◽  
Economic Sectors ◽  
Prediction Ability ◽  
Model Based ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

<abstract> <p>In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.</p> </abstract>

Daily streamflow prediction using optimally pruned extreme learning machine

2019 ◽  
Vol 577 ◽  
pp. 123981 ◽  
Author(s):  
Rana Muhammad Adnan ◽  
Zhongmin Liang ◽  
Slavisa Trajkovic ◽  
Mohammad Zounemat-Kermani ◽  
Binquan Li ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Streamflow Prediction ◽  
Daily Streamflow ◽  
Learning Machine
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