Reservoir drought prediction using support vector machines

Applied Mechanics and Materials

Volumn 145, Issue , 2012, Pages 455-459

  Chiang, Jie Lun ^a   Tsai, Yu Shiue ^a  

^a NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Artificial neural network; Drought; Prediction; Support vector machine; Water shortage

Indexed keywords

ANNUAL RAINFALL; ARTIFICIAL NEURAL NETWORK; BAYESIAN CLASSIFIER; COMPARATIVE STUDIES; CRITICAL LIMIT; DRY SEASONS; EFFICIENT USE OF WATER; EMPIRICAL RESULTS; HIGH POTENTIAL; INPUT DATAS; MAXIMUM LIKELIHOOD CLASSIFIERS; OPERATION RULE-CURVES; PREDICTION ACCURACY; RESERVOIR OPERATION; RESERVOIR STORAGE CAPACITY; SUPPORT VECTOR; SVM MODEL; TESTING DATA; THREE MODELS; TIME INTERVAL; TIME UNITS; TIME-PERIODS; TRAINING AND TESTING; TRAINING DATA; WATER SHORTAGE; WATER SHORTAGES; WATER USE;

BAYESIAN NETWORKS; CLIMATE CHANGE; DROUGHT; FORECASTING; INNOVATION; INPUT OUTPUT PROGRAMS; MAXIMUM LIKELIHOOD; NEURAL NETWORKS; RESERVOIRS (WATER); STATISTICAL TESTS; WATER RESOURCES; WATER SUPPLY;

SUPPORT VECTOR MACHINES;

EID: 84555218270     PISSN: 16609336     EISSN: 16627482     Source Type: Book Series    
DOI: 10.4028/www.scientific.net/AMM.145.455     Document Type: Conference Paper

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