Artificial neural network analysis for reliability prediction of regional runoff utilization

Environmental Monitoring and Assessment

Volumn 161, Issue 1-4, 2010, Pages 315-326

  Lee, S C ^a   Lin, H T ^a   Yang, T Y ^b  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

Author keywords

Back propagation neural network; Radial basis function neural network; Rainwater; Regional runoff utilization

Indexed keywords

ARTIFICIAL NEURAL NETWORK ANALYSIS; ARTIFICIAL NEURAL NETWORK MODELS; BACK PROPAGATION NEURAL NETWORKS; BASIC FUNCTIONS; DAILY RAINFALL; DEMAND AND SUPPLY; ERROR RATE; FUTURE APPLICATIONS; GAUSSIAN FUNCTIONS; INSTANTANEOUS CONTROL; LEARNING SPEED; OUTPUT DATA; PREDICTION MODEL; RADIAL BASIS FUNCTION NEURAL NETWORK; RADIAL BASIS FUNCTION NEURAL NETWORKS; RAINFALL RUNOFF; RELIABILITY PREDICTION; SMALL AREA; STORAGE CAPACITY; SUPPLY SYSTEM; TANK CAPACITY;

ATTITUDE CONTROL; BACKPROPAGATION; COMPETITION; COMPUTER SIMULATION; CONTROL EQUIPMENT; ELECTRIC NETWORK ANALYSIS; ERROR CORRECTION; MATHEMATICAL MODELS; QUALITY ASSURANCE; RADIAL BASIS FUNCTION NETWORKS; RELIABILITY ANALYSIS; RUNOFF; SYSTEM STABILITY; TORSIONAL STRESS;

NEURAL NETWORKS;

RAIN; RUNOFF;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; ERROR ANALYSIS; GAUSSIAN METHOD; PRECIPITATION INTENSITY; PREDICTION; RAINFALL-RUNOFF MODELING; RAINWATER; RELIABILITY ANALYSIS; RUNOFF;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CATCHMENT; ENVIRONMENTAL MANAGEMENT; STATISTICAL ANALYSIS; WATER MANAGEMENT; WATER SUPPLY;

ENVIRONMENTAL MONITORING; NEURAL NETWORKS (COMPUTER); RAIN; WATER MOVEMENTS;

EID: 74349116033     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-009-0748-5     Document Type: Article

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