Building more realistic reservoir optimization models using data mining - A case study of Shelbyville Reservoir

Advances in Water Resources

Volumn 34, Issue 6, 2011, Pages 701-717

  Hejazi, Mohamad I ^a   Cai, Ximing ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Data mining; Historical reservoir releases; Maximum relevance minimum redundancy (MRMR); Reservoir optimization; Stochastic dynamic programming (SDP)

Indexed keywords

ANNUAL COST; DIFFERENT TIME SCALE; HISTORICAL RECORDS; HISTORICAL RESERVOIR RELEASES; ILLINOIS; KNOWLEDGE DISCOVERY; MAXIMUM RELEVANCE MINIMUM REDUNDANCY (MRMR); MODEL FORMULATION; OBJECTIVE FUNCTIONS; OPTIMIZATION MODELS; PALMER DROUGHT SEVERITY INDICES; PARETO FRONT; RESERVOIR OPERATION; RESERVOIR OPTIMIZATION; STATE VARIABLES; STOCHASTIC DYNAMIC PROGRAMMING; TEMPORAL SCALE;

DATA MINING; DECISION MAKING; DYNAMIC PROGRAMMING; OPTIMIZATION; RESEARCH; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

RESERVOIRS (WATER);

DATA MINING; INFLOW; LAKE; LINEAR PROGRAMING; NUMERICAL MODEL; OPTIMIZATION; OUTFLOW; RESERVOIR;

ILLINOIS; KASKASKIA RIVER; LAKE SHELBYVILLE; UNITED STATES;

EID: 79955961266     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2011.03.001     Document Type: Article

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