AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

Journal of Hydrology

Volumn 530, Issue , 2015, Pages 634-644

  Tsai, Wen Ping ^a   Chang, Fi John ^a   Chang, Li Chiu ^b   Herricks, Edwin E ^c  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b TAMKANG UNIVERSITY   (Taiwan)

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

Author keywords

Artificial intelligence (AI); Artificial neural network (ANN); Ecosystems; Genetic algorithm (GA); Water resources management

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; ECOLOGY; ECONOMIC AND SOCIAL EFFECTS; FISH; GENETIC ALGORITHMS; HYDROGEOLOGY; NEURAL NETWORKS; RESERVOIR MANAGEMENT; RESERVOIRS (WATER); RIVERS; STREAM FLOW; WATER RESOURCES; WATER SUPPLY;

HYBRID ARTIFICIAL NEURAL NETWORK; HYBRID METHODOLOGIES; NON DOMINATED SORTING GENETIC ALGORITHM II (NSGA II); OPERATIONAL STRATEGIES; RIVER-FLOW MANAGEMENT; RIVERINE ECOSYSTEMS; SATISFACTION DEGREES; WATER RESOURCES MANAGEMENT;

ECOSYSTEMS;

ANTHROPOGENIC EFFECT; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; GENETIC ALGORITHM; OPTIMIZATION; SPECIES DIVERSITY; WATER MANAGEMENT; WATER RESOURCE; WATER SUPPLY;

SHIHMEN RESERVOIR; TAIWAN;

EID: 84944937647     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2015.10.024     Document Type: Article

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