AWWA WQTC63949

This slide presentation outlines advanced monitoring and data analysis techniques for improved operations of artificial neural networks (ANN). Study objectives included: develop ANN models specific to station location, algal class, and forecasting period; both exclude and include select water quality variables less frequently measured (phosphate, nitrate, sulfate, total organic carbon (TOC) and biochemical oxygen demand (BOD); two model output approaches - discrete algal counts and classification or ranges; and, two approaches - inputs measured at beginning of prediction period and end of prediction period. Several case studies are presented and include: groundwater/surface water mixing in Tucson, Arizona; algae bloom forecasting in New Jersey; and, saltwater upconing in Provincetown, Massachusetts. The future of ANN in water resources management is outlined. Includes figures.

Product Details

Edition:

Vol. - No.

Published:

11/01/2006

Number of Pages:

39

File Size:

1 file , 3 MB