Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow;Amélioration de la performance de techniques conditionnées par les données par pré-traitement pour la modélisation de l'apport journalier d'un réservoir

Hydrological Sciences Journal

Volumn 56, Issue 1, 2011, Pages 168-186

  Jothiprakash, V ^a   Kote, Alka S ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Data pre processing; Full year inflow data; India; Linear genetic programming; Model tree; Moving average; Pawana reservoir; Seasonal inflow; Time lagged recurrent network; Transformation

Indexed keywords

DATA PREPROCESSING; FULL-YEAR INFLOW DATA; INDIA; LINEAR GENETIC PROGRAMMING; MODEL TREE; MOVING AVERAGE; PAWANA RESERVOIR; SEASONAL INFLOW; TIME-LAGGED RECURRENT NETWORK; TRANSFORMATION;

ARTIFICIAL INTELLIGENCE; AUTOCORRELATION; FORECASTING; GENETIC PROGRAMMING; LINEAR TRANSFORMATIONS; METADATA; MULTILAYER NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; TIME SERIES; TREES (MATHEMATICS);

DATA HANDLING;

ARTIFICIAL NEURAL NETWORK; AUTOCORRELATION; DATA PROCESSING; INFLOW; LINEAR PROGRAMING; MODELING; PEAK FLOW; RESERVOIR;

INDIA;

EID: 79951559879     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626667.2010.546358     Document Type: Article

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