Application of Bayesian regularized BP neural network model for trend analysis, acidity and chemical composition of precipitation in North Carolina

Water, Air, and Soil Pollution

Volumn 172, Issue 1-4, 2006, Pages 167-184

  Xu, Min ^a   Zeng, Guangming ^a,b   Xu, Xinyi ^a   Huang, Guohe ^a,b   Jiang, Ru ^a   Sun, Wei ^b  

^a HUNAN UNIVERSITY   (China)

^b UNIVERSITY OF REGINA   (Canada)

Author keywords

Bayesian regularized back propagation neural network (BRBPNN); Chemical composition; Precipitation; Temporal trend; The sum of square weights

Indexed keywords

ACIDITY; BACKPROPAGATION; COMPOSITION; MATHEMATICAL MODELS; NEURAL NETWORKS; PH EFFECTS; REGRESSION ANALYSIS;

BAYESIAN REGULARIZED BACK-PROPAGATION NEURAL NETWORK (BRBPNN); REGULARIZATION PARAMETERS; TEMPORAL TRENDS; THE SUM OF SQUARE WEIGHTS;

PRECIPITATION (CHEMICAL);

AMMONIA; CALCIUM ION; CHLORIDE ION; MAGNESIUM ION; NITRATE; POTASSIUM ION; SILICATE; SODIUM ION;

ACIDITY; BACKPROPAGATION; COMPOSITION; MATHEMATICAL MODELS; NEURAL NETWORKS; PH EFFECTS; PRECIPITATION (CHEMICAL); REGRESSION ANALYSIS;

ACIDITY; BAYESIAN ANALYSIS; CHEMICAL COMPOSITION; NEUROLOGY; PRECIPITATION (CHEMISTRY); TREND ANALYSIS;

ACIDITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION NEURAL NETWORK; BAYES THEOREM; CHEMICAL COMPOSITION; CONCENTRATION (PARAMETERS); CORRELATION COEFFICIENT; PRECIPITATION; TREND STUDY; UNITED STATES;

ATLANTIC ISLANDS; ATLANTIC OCEAN; CARIBBEAN ISLANDS; CAROLINA; GREATER ANTILLES; PUERTO RICO;

EID: 33646447689     PISSN: 00496979     EISSN: 15732932     Source Type: Journal    
DOI: 10.1007/s11270-005-9068-8     Document Type: Article

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