Neural network modelling of Cryptosporidium and Giardia concentrations in the Delaware River, USA

Water Science and Technology

Volumn 43, Issue 12, 2001, Pages 125-132

  Neelakantan, T R ^a   Brion, G M ^a   Lingireddy, S ^a  

^a UNIVERSITY OF KENTUCKY   (United States)

Author keywords

Cryptosporidium; Giardia; Neural networks; Surface water; Water quality models

Indexed keywords

ALGORITHMS; BACKPROPAGATION; MATHEMATICAL MODELS; NEURAL NETWORKS; PATTERN RECOGNITION; WATER QUALITY; WATER TREATMENT PLANTS;

GIARDIA CONCENTRATIONS;

SURFACE WATERS;

RIVER WATER; SURFACE WATER;

MICROORGANISM; MODELING; MONITORING; RIVER WATER; WATER QUALITY;

ALGORITHM; ANALYSIS; ARTIFICIAL NEURAL NETWORK; BIOLOGY; CHEMICAL PARAMETERS; CLASSIFICATION; CONCENTRATION (PARAMETERS); CONFERENCE PAPER; CONTROLLED STUDY; CRYPTOSPORIDIUM; CYST; GIARDIA; MATHEMATICAL MODEL; NONHUMAN; OOCYST; PHYSICAL PARAMETERS; PREDICTION; PROTOZOON; TECHNIQUE; UNITED STATES; VALIDATION PROCESS; WATER MANAGEMENT; WATER QUALITY;

DELAWARE; DELAWARE RIVER; UNITED STATES;

CRYPTOSPORIDIUM; GIARDIA;

EID: 0034915469     PISSN: 02731223     EISSN: None     Source Type: Journal    
DOI: 10.2166/wst.2001.0723     Document Type: Conference Paper

References (10)

1. Effect of rainfall on Giardia and Cryptosporidium
2. A neural network approach to identify non-point sources of microbial contamination
3. USEPA method 1622
4. Waterborne outbreaks of cryptosporidiosis
5. Understanding protozoa in your watershed
6. Multilayer feedforward networks are universal approximators
7. Environmental methods for Cryptosporidium
8. Occurrence of Giardia and Cryptosporidium spp. in surface water supplies
9. A massive outbreak in Milwaukee of Cryptosporidium infection transmitted through the public water supply