Description
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
- Note:
- This product is unavailable in Ukraine, Russia, Belarus
