[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122930-en":3,"doc-seo-122930-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122930,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes - Article","The jar test serves as the standard approach for predicting conventional drinking water treatment performance and optimizing coagulant dose, but its time-consuming nature and reliance on human intervention limit continuous predictions. A machine learning model is developed from historical DWT plant data to run continuously with real-time sensor inputs, forecasting clarified water turbidity 15 minutes ahead. Three model families—multilayer perceptron, LSTM recurrent neural network, and GRU recurrent neural network—are compared under two training strategies: holdout and blocked cross-validation. Results show GRU-based RNN as the best overall option, reaching a mean absolute error as low as 0.044 NTU on an independent production set, while BCV-trained models typically match or outperform holdout-trained counterparts.","Titre:  Applying recurrent neural networks and blocked cross-validation to Title:  model conventional drinking water treatment processes  \nAuteurs:   \n Aleksandar Jakovljevic, Laurent Charlin, & Benoit Barbeau  \n Authors:   Date:  2024   Type:  Article de revue / Article   \n Jakovljevic, A. , Charlin, L. , & Barbeau, B. (2024) . Applying recurrent neural  \nRéférence:  networks and blocked cross-validation to model conventional drinking water Citation: treatment processes. Water, 16(7), 16071042 (14 pages) .  \n  [https://doi.org/10.3390/w16071042](https://doi.org/10.3390/w16071042)   \n| Document en libre accès dans PolyPublie\u003Cbr>Open Access document in PolyPublie\u003Cbr> |  |  |\n| --- | --- | --- |\n| URL de PolyPublie:\u003Cbr>PolyPublie URL: | [https://publications.polymtl.ca/58097/](https://publications.polymtl.ca/58097/) |  |\n|  Version officielle de l'éditeur / Published version\u003Cbr>Version: \u003Cbr> Révisé par les pairs / Refereed\u003Cbr> |  |  |\n| Conditions d’utilisation: \u003Cbr> CC BY\u003Cbr>Terms of Use: |  |  |\n\n\n| Document publié chez l’éditeur officiel\u003Cbr>Document issued by the official publisher\u003Cbr> |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| Titre de la revue:\u003Cbr> Water (vol. 16, no. 7)\u003Cbr>Journal Title:  |  |  |  |  |  |\n| Maison d’édition:\u003Cbr>Publisher: |  |  | MDPI |  |  |\n|  |  | URL officiel:\u003Cbr>Official URL:\u003Cbr> |  |  |  |\n|  | Mention légale:\u003Cbr>Legal notice: |  | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution\u003Cbr>(CC BY) license ([htt](https://creativecommons.org/licenses/by/4.0/)[ps:](https://creativecommons.org/licenses/by/4.0/)[//crea](https://creativecommons.org/licenses/by/4.0/)[tiv](https://creativecommons.org/licenses/by/4.0/)[ecom](https://creativecommons.org/licenses/by/4.0/)[m](https://creativecommons.org/licenses/by/4.0/)[ons.org/licenses/b](https://creativecommons.org/licenses/by/4.0/)[y/4](https://creativecommons.org/licenses/by/4.0/)[.0/](https://creativecommons.org/licenses/by/4.0/)).         |  |  |\n\nCe fichier a été téléchargé à partir de PolyPublie, le dépôt institutionnel de Polytechnique Montréal  \nThis file has been downloaded from PolyPublie, the institutional repository of Polytechnique Montréal  \n[https://publications.polymtl.ca](https://publications.polymtl.ca)  \n water   \nArticle  \nApplying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes  \nAleksandar Jakovljevic 1,*, Laurent Charlin 2 and Benoit Barbeau 1  \nCitation: Jakovljevic, A.; Charlin, L.; Barbeau, B. Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes. Water 2024, 16, 1042. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)w16071042  \nAcademic Editor: Long Ho  \nReceived: 8 February 2024  \nRevised: 27 March 2024  \nAccepted: 28 March 2024  \nPublished: 4 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, QC H3T 1J4, Canada; [benoit.barbeau@polymtl.ca](benoit.barbeau@polymtl.ca)  \n2 Department of Decision Sciences, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 2A7, Canada; [laurent.charlin@hec.ca](laurent.charlin@hec.ca)  \n* Correspondence: [aleksandar.jakovljevic@polymtl.ca](aleksandar.jakovljevic@polymtl.ca)  \nAbstract: The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and optimizing the coagulant dose. This ","cbCaieVpewm0hTcT","https://ap.wps.com/l/cbCaieVpewm0hTcT","pdf",2855841,1,15,"English","en",105,"# Introduction\n## Drinking water treatment unit processes and modeling need\n# Methods\n## ML models for turbidity forecasting (MLP, LSTM, GRU)\n## Training strategies (holdout vs blocked cross-validation)\n# Results\n## Model comparison on independent production data\n## Error analysis and impact of blocked cross-validation\n# Conclusions\n## Key findings and implications for DWT process modeling","[{\"question\":\"Why is the jar test not suitable for continuous drinking water treatment predictions?\",\"answer\":\"The jar test is time-consuming and requires human intervention, making it impractical for continuous process forecasting.\"},{\"question\":\"What is predicted and how far ahead in time?\",\"answer\":\"The model predicts clarified water turbidity 15 minutes in advance using real-time sensor data.\"},{\"question\":\"Which model performed best and what was the reported error?\",\"answer\":\"A GRU-based RNN performed best overall, achieving a mean absolute error as low as 0.044 NTU on an independent production set.\"}]","Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes - 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