Comparing artificial and deep neural network models for prediction of coagulant amount and settled water turbidity: Lessons learned from big data in water treatment operations

[1]  R. P. Pandey,et al.  A review of artificial intelligence in water purification and wastewater treatment: Recent advancements , 2022, Journal of Water Process Engineering.

[2]  M. Fujita,et al.  Machine learning estimation of biodegradable organic matter concentrations in municipal wastewater. , 2022, Journal of environmental management.

[3]  John A. Young,et al.  Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA. , 2022, Journal of environmental management.

[4]  Heba M. Ismail,et al.  Water Quality Classification Using Machine Learning Algorithms , 2022, 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA).

[5]  Ruwen Qin,et al.  A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring , 2022, Water.

[6]  Jersson X. Leon-Medina,et al.  Attention-Based Deep Recurrent Neural Network to Forecast the Temperature Behavior of an Electric Arc Furnace Side-Wall , 2022, Sensors.

[7]  Jun Ma,et al.  Machine learning in natural and engineered water systems. , 2021, Water research.

[8]  Shakeri Narges,et al.  Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS) , 2021, Journal of Environmental Health Science and Engineering.

[9]  Y. Shavitt,et al.  Prediction of wastewater treatment quality using LSTM neural network , 2021, Environmental Technology & Innovation.

[10]  Kung-Jeng Wang,et al.  A data-driven optimization model for coagulant dosage decision in industrial wastewater treatment , 2021, Comput. Chem. Eng..

[11]  Gulzar Alam,et al.  Applications of artificial intelligence in water treatment for the optimization and automation of the adsorption process: Recent advances and prospects , 2021 .

[12]  Lei Li,et al.  Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review , 2021, Chemical Engineering Journal.

[13]  Graham A. Gagnon,et al.  Comparing the Predictive Performance, Interpretability, and Accessibility of Machine Learning and Physically Based Models for Water Treatment , 2020 .

[14]  Qi Zhang,et al.  Multivariate Time-series Anomaly Detection via Graph Attention Network , 2020, 2020 IEEE International Conference on Data Mining (ICDM).

[15]  H. Yamamura,et al.  Dosage optimization of polyaluminum chloride by the application of convolutional neural network to the floc images captured in jar tests , 2020 .

[16]  Min Zuo,et al.  Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. , 2019, Water research.

[17]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[18]  CD Jayaweera,et al.  Improved predictive capability of coagulation process by extreme learning machine with radial basis function , 2019 .

[19]  N. Hilal,et al.  Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination? , 2019, Desalination.

[20]  Yuqing Chang,et al.  Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting , 2018, Energies.

[21]  Manukid Parnichkun,et al.  Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system , 2017, Applied Water Science.

[22]  Chan Moon Kim,et al.  MLP, ANFIS, and GRNN based real-time coagulant dosage determination and accuracy comparison using full-scale data of a water treatment plant , 2017 .

[23]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[24]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[25]  Robert C. Andrews,et al.  The application of artificial neural networks for the optimization of coagulant dosage , 2011 .

[26]  Y. Yao,et al.  On Early Stopping in Gradient Descent Learning , 2007 .

[27]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[28]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[29]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[30]  Salim Heddam,et al.  Extremely randomized tree: a new machines learning method for predicting coagulant dosage in drinking water treatment plant , 2021 .