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USING NEURAL NETWORKS to predict peak Cryptosporidium concentrations
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 this water treatment plant intake was noted (full oocysts = 0.595 X total oocysts, R2 = 0.9011).
USING NEURAL NETWORKS to predict peak Cryptosporidium concentrations
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 this water treatment plant intake was noted (full oocysts = 0.595 X total oocysts, R2 = 0.9011).
USING NEURAL NETWORKS to predict peak Cryptosporidium concentrations
Brion, Gail Montgomery (author) / Neelakantan, T.R. (author) / Lingireddy, Srinivasa (author)
Journal ‐ American Water Works Association ; 93 ; 99-105
2001-01-01
7 pages
Article (Journal)
Electronic Resource
English
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