Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Abstract

Proper chemical demand prediction is important for water management and the environment. The study aimed to select and apply proper data-driven models based on real-world big data for dosage prediction...

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

“…24,25 A number of models have been applied to predict the turbidity of the effluent [26][27][28][29][30][31] and the requisite coagulant dosage. 24,26,[32][33][34] A hybrid of k-means clustering and an adaptive neuro-fuzzy inference system was applied to predict the turbidity of water and the optimal coagulant dosage, and yielded values of R 2 > 0.8. 24 Andrews and Griffiths, 26 and Narges et al 32 developed a seasonal ANN model and an artificial fuzzy neural network based on subtractive clustering, respectively, to predict the optimal coagulant dosage needed to achieve the desired turbidity of the effluent in a sedimentation tank, with values of R 2 ranging from 0.78 to 0.89 and from 0.63 to 0.79, respectively.…”