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dc.contributor.authorNguyen Van, Lam
dc.contributor.authorTornyeviadzi, Hoese Michel
dc.contributor.authorTien Bui, Dieu
dc.contributor.authorSeidu, Razak
dc.date.accessioned2022-10-17T14:07:39Z
dc.date.available2022-10-17T14:07:39Z
dc.date.created2022-01-20T10:10:26Z
dc.date.issued2022
dc.identifier.citationWater. 2022, 14 (3), .en_US
dc.identifier.issn2073-4441
dc.identifier.urihttps://hdl.handle.net/11250/3026461
dc.description.abstractPredicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been proposed as a robust technique for predicting discharges in wastewater networks. This study explored the potential application of an LSTM model to predict discharges using 3-month data set in a sewer network in Ålesund city, Norway. Different sequence-to-sequence LSTMs were investigated using various input and output datasets. The impact of data aggregation (10-min and 30-min intervals) was examined and compared to original sensor data (5-min intervals) to evaluate the performance of the LSTM model. The results show that 50-neuron LSTM architecture performed better (MAPE = 0.09, RMSE = 0.0008, R2 = 0.8) in predicting discharges for the study area. The study indicates that using the same sequence length for the prior and the forecast can improve the effectiveness of the LSTM model. Based on the results, using a 10-min aggregated discharge dataset reduces energy consumption, transmission bandwidth, and storage capacity. Additionally, it improves prediction performance compared to an original 5-min interval data in Ålesund city.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePredicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Frameworken_US
dc.title.alternativePredicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Frameworken_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber17en_US
dc.source.volume14en_US
dc.source.journalWateren_US
dc.source.issue3en_US
dc.identifier.doi10.3390/w14030300
dc.identifier.cristin1985763
dc.relation.projectNorges teknisk-naturvitenskapelige universitet: 90392200en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal