Predicting Water Quality With Non-stationarity: Event-Triggered Deep Fuzzy Neural Network

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dc.contributor.authorWang, G.
dc.contributor.authorChen, H.
dc.contributor.authorHan, H.
dc.contributor.authorBi, J.
dc.contributor.authorQiao, J.
dc.contributor.authorTirkolaee, E.B.
dc.date.accessioned2024-05-19T14:33:22Z
dc.date.available2024-05-19T14:33:22Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractWater quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationarity of water quality data suffers from time variation and human activities, which leads to a poor prediction accuracy because ANNs receive whole water quality data passively, including abnormal conditions. We consider such a tough problem in this article and propose an event-triggered deep fuzzy neural network (ET-DFNN) to pursue the better performance of water quality prediction in the complex water environment. First, a deep pretraining model is constructed to extract the effective features from raw water quality data. Second, we construct a DFNN model where the extracted effective features are considered as the input variables. Third, some events are defined to characterize the abnormal conditions of state evolution in water quality. The DFNN is trained and updated using different learning strategies only when the corresponding events are triggered, otherwise it ignores the current data sample and directly goes to the next data sample. The practical data-based experimental results show that the ET-DFNN achieves better prediction performance in accuracy and efficiency than its peers. Especially, the training efficiency of ET-DFNN is improved by 57.94% on total phosphorus prediction and 48.31% on biochemical oxygen demand prediction, respectively. IEEEen_US
dc.identifier.doi10.1109/TFUZZ.2024.3354919
dc.identifier.endpage10en_US
dc.identifier.issn1063-6706
dc.identifier.scopus2-s2.0-85184311461en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/TFUZZ.2024.3354919
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4208
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Transactions on Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectDeep Fuzzy Neural Network (Dfnn)en_US
dc.subjectEvent-Triggered Strategyen_US
dc.subjectFeature Extractionen_US
dc.subjectFuzzy Neural Networksen_US
dc.subjectNonstationarityen_US
dc.subjectPredictive Modelsen_US
dc.subjectTrainingen_US
dc.subjectWater Environmenten_US
dc.subjectWater Qualityen_US
dc.subjectWater Quality Predictionen_US
dc.subjectWater Resourcesen_US
dc.titlePredicting Water Quality With Non-stationarity: Event-Triggered Deep Fuzzy Neural Networken_US
dc.typeArticleen_US

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