Stochastic and artificial neural network models for reservoir inflow prediction

Journal of the Institution of Engineers (India): Civil Engineering Division

Volumn 90, Issue NOVEMBER, 2009, Pages 25-33

  Kote, A S ^a   Jothiprakash, V ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Auto regressive integrated moving average (arima); Back propagation through time (bptt); Inflow prediction; Reservoirs; Time lagged recurrent neural networks (tlrn)

Indexed keywords

ARIMA MODELS; ARTIFICIAL NEURAL NETWORK MODELS; AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODELS; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; BACK-PROPAGATION THROUGH TIME; GOODNESS-OF-FIT MEASURE; MEMORY STRUCTURE; MODEL RESULTS; RESERVOIR INFLOW; RESERVOIR INFLOW PREDICTION; SCATTER PLOTS; TIME LAGGED RECURRENT NETWORK; TIME LAGGED RECURRENT NEURAL NETWORKS (TLRN); TIME STEP; TRAINED NEURAL NETWORKS; UNIVARIATE; UPPER BHIMA RIVER BASINS;

BACKPROPAGATION; FORECASTING; GENETIC ALGORITHMS; NORMAL DISTRIBUTION; PETROLEUM RESERVOIRS; RECURRENT NEURAL NETWORKS;

TIME SERIES;

EID: 84859778319     PISSN: 0020336X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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