연구동향 분석

Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms

Sinan Nacar M. Ali Hınıs Murat Kankal 2018년

논문상세정보
    • 저자 Sinan Nacar M. Ali Hınıs Murat Kankal
    • 제어번호 105917580
    • 학술지명 KSCE Journal of Civil Engineering
    • 권호사항 Vol. 22 No. 9 [ 2018 ]
    • 발행처 대한토목학회
    • 자료유형 학술저널
    • 수록면 3676-3685
    • 언어 English
    • 출판년도 2018
    • 등재정보 KCI등재
    • 소장기관 영남대학교 과학도서관
    • 판매처
    유사주제 논문( 0)
인용/피인용

' Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms' 의 참고문헌

  • Using multivariate statistics (2nd ed.)
  • Time-lagged recurrent network for forecasting episodic event suspended sediment load in typhoon prone area
    Wang, Y. M. [2009]
  • The effect of daylight saving time options on electricity consumption of Turkey
    Karasu S. [2010]
  • Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis
    Helena, B. [2000]
  • Streamflow forecasting using different artificial neural network algorithms
    Kisi, O. [2007]
  • Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model
    Shiri, J. [2010]
  • River stage forecasting using artificial neural networks
  • River flow prediction using artificial neural networks: Generalisation beyond the calibration range
  • River flow modeling using artificial neural networks
    Kisi, O. [2004]
  • River flow forecasting and estimation using different artificial neural network techniques
    Kisi, O. [2008]
  • River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches
  • Restructuring of Turkey's electricity market and the share of hydropower energy: The case of the Eastern Black Sea Basin
    Uzlu, E. [2011]
  • Reservoir inflow prediction using time lagged recurrent neural networks
    Kote, A. S. [2008]
  • Principal component analysis
    A. Herve [2010]
  • Prediction of suspended sediment concentration from water quality variables
    Bayram, A. [2014]
  • Performance evaluation of artificial neural network approaches in forecasting reservoir inflow
  • On lines and planes of closest fit to systems of points in space
    Pearson, K. [1901]
  • Neuro Solutions v5.0, Neuro Solutions Getting Started Manual
  • Neural-network models of rainfall-runoff process
    Smith, J. [1995]
  • Neural networks and wavelet conjunction model for intermittent streamflow forecasting
    Kisi, O. [2009]
  • Neural network applications in determining the fatigue crack opening load
    Kang, J. Y. [1998]
  • Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation
    Kisi, O. [2004]
  • Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks
  • Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm
    Kisi, O. [2012]
  • Methods to improve the neural network performance in suspended sediment estimation
  • Machine learning neural networks genetic algorithms and fuzzy systems
    Adeli, H. [1994]
  • Learning representations by back-propagating errors
  • Forecasting flows in Apalachicola River using neural networks
    Huang, W. [2004]
  • Forecasting daily streamflow using hybrid ANN models
    Wang, W. [2006]
  • Forecasting daily stream flows using artificial intelligence approaches
    Shiri, J. [2012]
  • Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data
  • Evapotranspiration modelling from climatic data using a neural computing technique
    Kisi, O. [2007]
  • Estimating sediment discharge using sediment rating curves and artificial neural networks in the Shiwen River
  • Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey
    Uzlu, E. [2014]
  • Effect of input variables preprocessing in artificial neural network on monthly flow prediction by PCA and wavelet transformation
    Noori, R. [2009]
  • Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
  • Comparison of the three backpropagation training algorithms for two case studies
    Kisi, O. [2005]
  • Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic
    Noori, R. [2010]
  • Assessment of big floods in the Eastern Black Sea Basin of Turkey
    Yuksek, O. [2013]
  • Artificial neural networks in hydrology. Vol. 36
  • Artificial neural networks as rainfall-runoff models
  • Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India
  • Application of a neural network model in establishing a stage-discharge relationship for a tidal river
  • Analysis of a complex of statistical variables into principal components
  • An intelligent approach for galaxies images classification
  • Advanced methods in neural computing
  • A neural networks approach for deriving irrigation reservoir operating rules