Modeling long-term rainfall-runoff time series through wavelet-weighted regularization extreme learning machine

2021 ◽  
Author(s):  
Amir Alizadeh ◽  
Ahmad Rajabi ◽  
Saeid Shabanlou ◽  
Behrouz Yaghoubi ◽  
Fariborz Yosefvand
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Rainfall Runoff ◽  
Learning Machine

scholarly journals Long-term voltage stability monitoring of power system areas using a kernel extreme learning machine approach

2021 ◽  
Author(s):  
Walter M. Villa-Acevedo ◽  
Jesús M. López-Lezama ◽  
Delia G. Colomé ◽  
Jaime Cepeda
Keyword(s):  
Power System ◽  
Extreme Learning Machine ◽  
Voltage Stability ◽  
Stability Monitoring ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Time series forecasting based on deep extreme learning machine

2017 ◽  
Author(s):  
Xuqi Guo ◽  
Yusong Pang ◽  
Gaowei Yan ◽  
Tiezhu Qiao
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Time Series Forecasting ◽  
Learning Machine

Power Quality Analysis Based on Time Series Similarity and Extreme Learning Machine

2018 ◽  
pp. 157-170
Author(s):  
Meimei Li ◽  
Shouqing Jia ◽  
Shuqing Zhang ◽  
Zheng Gong ◽  
Guaifu Li
Keyword(s):  
Time Series ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Quality Analysis ◽  
Power Quality Analysis ◽  
Learning Machine

An improved kernel-based incremental extreme learning machine with fixed budget for nonstationary time series prediction

2017 ◽  
Vol 31 (3) ◽  
pp. 637-652 ◽  
Author(s):  
Wei Zhang ◽  
Aiqiang Xu ◽  
Dianfa Ping ◽  
Mingzhe Gao
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Time Series Prediction ◽  
Nonstationary Time Series ◽  
Fixed Budget ◽  
Learning Machine

Smart meter based on time series modify and extreme learning machine

2017 ◽  
Author(s):  
Samudra R. Arrachman ◽  
M.F. Adiatmoko ◽  
Adi Soeprijanto ◽  
Mat Syai'in ◽  
M.S.A. Sidik ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Smart Meter ◽  
Learning Machine

scholarly journals Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique

2020 ◽  
Vol 17 (13) ◽  
pp. 4788 ◽  
Author(s):  
Junwei Ma ◽  
Xiao Liu ◽  
Xiaoxu Niu ◽  
Yankun Wang ◽  
Tao Wen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Quantile Regression ◽  
Extreme Learning Machine ◽  
Data Driven ◽  
Ensemble Prediction ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Landslide Displacement Prediction ◽  
Learning Machine

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

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