Monthly discharge forecasting using wavelet neural networks with extreme learning machine
Published 2014 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Monthly discharge forecasting using wavelet neural networks with extreme learning machine
Authors
Keywords
monthly discharges, discrete wavelet transform, extreme learning machine, forecasting
Journal
Science China-Technological Sciences
Volume 57, Issue 12, Pages 2441-2452
Publisher
Springer Nature
Online
2014-12-03
DOI
10.1007/s11431-014-5712-0
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows
- (2013) Shouke Wei et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Evaluation of optimization operation models for cascaded hydropower reservoirs to utilize medium range forecasting inflow
- (2013) Wei Xu et al. Science China-Technological Sciences
- Parameter estimation method based on parameter function surface
- (2013) WeiMin Bao et al. Science China-Technological Sciences
- Wind speed prediction by chaotic operator network based on Kalman Filter
- (2013) ChunBo Xiu et al. Science China-Technological Sciences
- A physics-based hydro-geomorphologic simulation utilizing cluster parallel computing
- (2013) QiHua Ran et al. Science China-Technological Sciences
- Application of profile likelihood function to the uncertainty analysis of hydrometeorological extreme inference
- (2013) Fan Lu et al. Science China-Technological Sciences
- Uncertainty analysis of hydrological model parameters based on the bootstrap method: A case study of the SWAT model applied to the Dongliao River Watershed, Jilin Province, Northeastern China
- (2013) Zheng Zhang et al. Science China-Technological Sciences
- Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin
- (2013) QingFang Hu et al. Science China-Technological Sciences
- Comparative study of different wavelets for hydrologic forecasting
- (2012) R. Maheswaran et al. COMPUTERS & GEOSCIENCES
- Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon
- (2012) Riccardo Taormina et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- A new automatic target recognition system based on wavelet extreme learning machine
- (2012) Engin Avci et al. EXPERT SYSTEMS WITH APPLICATIONS
- Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification
- (2012) Y. Bodyanskiy et al. INFORMATION SCIENCES
- A novel visual modeling system for time series forecast: application to the domain of hydrology
- (2012) Mutao Huang et al. JOURNAL OF HYDROINFORMATICS
- Wavelet–Volterra coupled model for monthly stream flow forecasting
- (2012) R. Maheswaran et al. JOURNAL OF HYDROLOGY
- Uncertainty analysis of hydrological processes based on ARMA-GARCH model
- (2012) HongRui Wang et al. Science China-Technological Sciences
- Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC
- (2012) HuiLan Zhang et al. Science China-Technological Sciences
- A new criterion for defining the breakpoint of the wetted perimeter-discharge curve and its application to estimating minimum instream flow requirements
- (2012) BaoHui Men et al. Science China-Technological Sciences
- Parallel architecture and optimization for discrete-event simulation of spike neural networks
- (2012) YuHua Tang et al. Science China-Technological Sciences
- Separating impacts of vegetation change and climate variability on streamflow using hydrological models together with vegetation data
- (2012) HuiYun Li et al. Science China-Technological Sciences
- Hybrid Wavelet–Genetic Programming Approach to Optimize ANN Modeling of Rainfall–Runoff Process
- (2011) Vahid Nourani et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis
- (2011) C.L. Wu et al. JOURNAL OF HYDROLOGY
- A wavelet neural network conjunction model for groundwater level forecasting
- (2011) Jan Adamowski et al. JOURNAL OF HYDROLOGY
- Flood simulation using parallel genetic algorithm integrated wavelet neural networks
- (2011) Yuhui Wang et al. NEUROCOMPUTING
- A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm
- (2010) W.K. Wong et al. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
- Daily river flow forecasting using wavelet ANN hybrid models
- (2010) Niranjan Pramanik et al. JOURNAL OF HYDROINFORMATICS
- A hybrid model coupled with singular spectrum analysis for daily rainfall prediction
- (2010) K. W. Chau et al. JOURNAL OF HYDROINFORMATICS
- River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model
- (2010) Taher Rajaee et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds
- (2010) Jan Adamowski et al. JOURNAL OF HYDROLOGY
- A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
- (2010) Heesung Yoon et al. JOURNAL OF HYDROLOGY
- Intelligent approaches using support vector machine and extreme learning machine for transmission line protection
- (2010) V. Malathi et al. NEUROCOMPUTING
- Research on monthly flow uncertain reasoning model based on cloud theory
- (2010) YuZhi Shi et al. Science China-Technological Sciences
- Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey
- (2009) Özgür Kişi HYDROLOGICAL PROCESSES
- Modelling evapotranspiration using discrete wavelet transform and neural networks
- (2009) Turgay Partal HYDROLOGICAL PROCESSES
- Prediction of daily precipitation using wavelet—neural networks
- (2009) TURGAY PARTAL et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market
- (2009) L.M. Saini et al. IET Generation Transmission & Distribution
- Artificial neural network model for river flow forecasting in a developing country
- (2009) Asaad Y. Shamseldin JOURNAL OF HYDROINFORMATICS
- Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting
- (2009) Özgür Kişi JOURNAL OF HYDROLOGIC ENGINEERING
- Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey
- (2009) Mesut Çimen et al. JOURNAL OF HYDROLOGY
- A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
- (2009) Wen-Chuan Wang et al. JOURNAL OF HYDROLOGY
- Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model
- (2009) Wensheng Wang et al. WATER RESOURCES MANAGEMENT
- A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling
- (2009) Vahid Nourani et al. WATER RESOURCES MANAGEMENT
- A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation
- (2008) Vahid Nourani et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Stream flow forecasting using neuro-wavelet technique
- (2008) Özgür Kişi HYDROLOGICAL PROCESSES
- Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed
- (2008) E. Mutlu et al. HYDROLOGICAL PROCESSES
- Improving artificial neural networks’ performance in seasonal time series forecasting
- (2008) C HAMZACEBI INFORMATION SCIENCES
- Estimation and forecasting of daily suspended sediment data using wavelet–neural networks
- (2008) Turgay Partal et al. JOURNAL OF HYDROLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started