Publication:
An advanced deep learning model for predicting water quality index

Simple item page
dc.citedby10
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:43:57Z
dc.date.available2025-03-03T07:43:57Z
dc.date.issued2024
dc.description.abstractPredicting a water quality index (WQI) is important because it serves as an important metric for assessing the overall health and safety of water bodies. Our paper develops a new hybrid model for predicting the WQI. The study uses a combination of a convolutional neural network (CNN), clockwork recurrent neural network (Clockwork RNN), and M5 Tree (CNN-CRNN-M5T) to predict a WQI. The M5T model lacks advanced operators for extracting meaningful data from water quality parameters, so the new model enhances its ability to analyze intricate patterns. The general linear model analysis of variance (GLM-ANOVA) is an improved version of the ANOVA. Our study uses the GLM-ANOVA to determine significant inputs. As all input variables had p < 0.050, they were defined as significant variables. Results showed that NH-NL and PH had the highest and lowest impact, respectively. Our study used the CNN-CRNN-M5T, CNN-CRNN, CRNN-M5T, CNN-M5T, CRNN, CNN, and M5T models to predict the WQI of a large basin in Malaysia. The CNN-CRNN decreased testing mean absolute error (MAE) of the CRNN, CNN, and M5T models by 2.1 %, 12 %, and 15 %, respectively. The CNN-CRNN-M5T model increased Nash?Sutcliffe efficiency coefficient of the other models by 4?20 % and 2.1?19 %, respectively. The CNN-CRNN-M5T model was a reliable tool for spatial and temporal predictions of WQI. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo111806
dc.identifier.doi10.1016/j.ecolind.2024.111806
dc.identifier.scopus2-s2.0-85186598296
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85186598296&doi=10.1016%2fj.ecolind.2024.111806&partnerID=40&md5=643e3bac28383ec6c8e939060b501577
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36692
dc.identifier.volume160
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEcological Indicators
dc.subjectMalaysia
dc.subjectAnalysis of variance (ANOVA)
dc.subjectConvolutional neural networks
dc.subjectLearning systems
dc.subjectQuality assurance
dc.subjectRecurrent neural networks
dc.subjectWater management
dc.subjectWater quality
dc.subjectConvolutional neural network
dc.subjectDeep learning model
dc.subjectGeneral linear model analysis
dc.subjectHealth and safety
dc.subjectHybrid model
dc.subjectLearning models
dc.subjectWater quality indexes
dc.subjectWater quality parameters
dc.subjectWater resources management
dc.subjectWaterbodies
dc.subjecterror analysis
dc.subjectindex method
dc.subjectmachine learning
dc.subjectparameterization
dc.subjectvariance analysis
dc.subjectwater management
dc.subjectwater quality
dc.subjectwater resource
dc.subjectForecasting
dc.titleAn advanced deep learning model for predicting water quality indexen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS