Abstract
The surface water treatment plant (WTP) has an important role in the quality of water supplied to the consumers. The quality of the intake water to a WTP plays an important role in plant operation. The nature of the inflow water decides the duration of treatment, dozing pattern, etc. The temporal variation of only one parameter like turbidity can induce major adaptation in plant operation and may also affect the production efficiency. In the present study, a novel artificial neural network based model was proposed to predict temporal patterns of Turbidity concentration at the intake point of a WTP which supplies to a peri-urban settlement where turbidity varies due to the urban runoff. According to performance metrics, the model could predict the temporal pattern with 99% accuracy and at a lag of one hour.
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