Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386960690> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4386960690 endingPage "3325" @default.
- W4386960690 startingPage "3325" @default.
- W4386960690 abstract "Artificial Intelligence (AI) has recently emerged as a powerful tool with versatile applications spanning various domains. AI replicates human intelligence processes through machinery and computer systems, finding utility in expert systems, image and speech recognition, machine vision, and natural language processing (NLP). One notable area with limited exploration pertains to using deep learning models, specifically Recurrent Neural Networks (RNNs), for predicting water quality in wastewater treatment plants (WWTPs). RNNs are purpose-built for handling sequential data, featuring a feedback mechanism. However, standard RNNs may exhibit limitations in accommodating both short-term and long-term dependencies when addressing intricate time series problems. The solution to this challenge lies in adopting Long Short-Term Memory (LSTM) cells, known for their inherent memory management through a ‘forget gate’ mechanism. In general, LSTM architecture demonstrates superior performance. WWTP data represent a historical series influenced by fluctuating environmental conditions. This study employs simple RNNs and LSTM architecture to construct prediction models for effluent parameters, systematically assessing their performance through various training data scenarios and model architectures. The primary objective was to determine the most suitable WWTP dataset model. The study revealed that an epoch setting of 50 and a batch size of 100 yielded the lowest training time and root mean square error (RMSE) values for both RNN and LSTM models. Furthermore, when these models are applied to predict effluent parameters, they exhibit precise RMSE values for all parameters. The study results can be applied to detect potential upsets in WWTP operations." @default.
- W4386960690 created "2023-09-23" @default.
- W4386960690 creator A5019107814 @default.
- W4386960690 creator A5072613648 @default.
- W4386960690 date "2023-09-22" @default.
- W4386960690 modified "2023-09-29" @default.
- W4386960690 title "Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models" @default.
- W4386960690 cites W1479948543 @default.
- W4386960690 cites W1573302072 @default.
- W4386960690 cites W2007574120 @default.
- W4386960690 cites W2042052842 @default.
- W4386960690 cites W2102148524 @default.
- W4386960690 cites W2119922937 @default.
- W4386960690 cites W2769929350 @default.
- W4386960690 cites W2792071392 @default.
- W4386960690 cites W2802721802 @default.
- W4386960690 cites W2825946107 @default.
- W4386960690 cites W2911287026 @default.
- W4386960690 cites W2911352573 @default.
- W4386960690 cites W2914192042 @default.
- W4386960690 cites W2924347204 @default.
- W4386960690 cites W2942403158 @default.
- W4386960690 cites W2986617680 @default.
- W4386960690 cites W3087665109 @default.
- W4386960690 cites W3175134873 @default.
- W4386960690 cites W3214513102 @default.
- W4386960690 cites W4212871224 @default.
- W4386960690 cites W4386132016 @default.
- W4386960690 doi "https://doi.org/10.3390/w15193325" @default.
- W4386960690 hasPublicationYear "2023" @default.
- W4386960690 type Work @default.
- W4386960690 citedByCount "0" @default.
- W4386960690 crossrefType "journal-article" @default.
- W4386960690 hasAuthorship W4386960690A5019107814 @default.
- W4386960690 hasAuthorship W4386960690A5072613648 @default.
- W4386960690 hasBestOaLocation W43869606901 @default.
- W4386960690 hasConcept C105795698 @default.
- W4386960690 hasConcept C108583219 @default.
- W4386960690 hasConcept C119857082 @default.
- W4386960690 hasConcept C127413603 @default.
- W4386960690 hasConcept C139945424 @default.
- W4386960690 hasConcept C147168706 @default.
- W4386960690 hasConcept C147455438 @default.
- W4386960690 hasConcept C154945302 @default.
- W4386960690 hasConcept C33923547 @default.
- W4386960690 hasConcept C41008148 @default.
- W4386960690 hasConcept C50644808 @default.
- W4386960690 hasConcept C87717796 @default.
- W4386960690 hasConceptScore W4386960690C105795698 @default.
- W4386960690 hasConceptScore W4386960690C108583219 @default.
- W4386960690 hasConceptScore W4386960690C119857082 @default.
- W4386960690 hasConceptScore W4386960690C127413603 @default.
- W4386960690 hasConceptScore W4386960690C139945424 @default.
- W4386960690 hasConceptScore W4386960690C147168706 @default.
- W4386960690 hasConceptScore W4386960690C147455438 @default.
- W4386960690 hasConceptScore W4386960690C154945302 @default.
- W4386960690 hasConceptScore W4386960690C33923547 @default.
- W4386960690 hasConceptScore W4386960690C41008148 @default.
- W4386960690 hasConceptScore W4386960690C50644808 @default.
- W4386960690 hasConceptScore W4386960690C87717796 @default.
- W4386960690 hasIssue "19" @default.
- W4386960690 hasLocation W43869606901 @default.
- W4386960690 hasOpenAccess W4386960690 @default.
- W4386960690 hasPrimaryLocation W43869606901 @default.
- W4386960690 hasRelatedWork W2795261237 @default.
- W4386960690 hasRelatedWork W3014300295 @default.
- W4386960690 hasRelatedWork W3164822677 @default.
- W4386960690 hasRelatedWork W4223943233 @default.
- W4386960690 hasRelatedWork W4225161397 @default.
- W4386960690 hasRelatedWork W4312200629 @default.
- W4386960690 hasRelatedWork W4360585206 @default.
- W4386960690 hasRelatedWork W4364306694 @default.
- W4386960690 hasRelatedWork W4380075502 @default.
- W4386960690 hasRelatedWork W4380086463 @default.
- W4386960690 hasVolume "15" @default.
- W4386960690 isParatext "false" @default.
- W4386960690 isRetracted "false" @default.
- W4386960690 workType "article" @default.