Abstract
Artificial neural networks (ANNs) were successfully applied to data observations from a small watershed consisting of commonly measured indicator bacteria, weather conditions, and turbidity to distinguish between human sewage and animal-impacted runoff, fresh runoff from aged, and agricultural land-use-associated fresh runoff from that of suburban land-use-associated-fresh runoff. The ANNs were applied in a cascading, or hierarchical scheme. ANN performance was measured in two ways: (1) training and (2) testing. An ANN was able to sort sewage from runoff with <1% error. Turbidity was found to be relatively unimportant for sorting sewage from runoff, while gross measurements of gram-negative and gram-positive bacteria were required. Predictions clustered tightly around the known values. ANN classification of aged suburban runoff from fresh, and agricultural runoff from suburban was accomplished with >90% accuracy.
Original language | English |
---|---|
Pages (from-to) | 3765-3774 |
Number of pages | 10 |
Journal | Water Research |
Volume | 36 |
Issue number | 15 |
DOIs | |
State | Published - Sep 2002 |
Keywords
- Fecal indicators
- Land-use
- Neural networks
- Runoff
- Sewage
ASJC Scopus subject areas
- Environmental Engineering
- Civil and Structural Engineering
- Ecological Modeling
- Water Science and Technology
- Waste Management and Disposal
- Pollution