Neural network modeling was used to examine the relationships between multiple
interrelated water quality and quantity parameters at the intake to a water treatment
facility located on the Delaware River. The relationships were used to train a
neural network model to predict peak concentrations of Cryptosporidium oocysts
at the intake of a New Jersey water treatment facility. Input parameters to
the model were selected based on their correlation with oocyst concentrations and
stepwise evaluation of neural network training. The final trained neural
network model predicted two conditions of input Cryptosporidium concentrations,
background and above background (assigned as 1 and 0, respectively), from eight
other water quality parameters. Clostridium perfringens concentrations were the
most significant input parameter in predicting the final model's performance. Turbidity
was the least significant parameter. Furthermore, a site-specific, linear relationship
between the numbers of full oocysts and the total number of oocysts recovered by the
Information Collection Rule method at the water treatment plant intake was noted
(full oocysts = 0.595 x total oocysts, R2 = 0.9011). Includes 15 references, tables, figures.
| Edition : | Vol. 93 - No. 1 |
| File Size : | 1
file
, 150 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 7 |
| Published : | 01/01/2001 |
