Publication:
Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

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dc.citedby14
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAzmi M.S.B.N.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57220031281en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:07:19Z
dc.date.available2023-05-29T08:07:19Z
dc.date.issued2020
dc.descriptionAgricultural robots; Ammonia; Biochemical oxygen demand; Dissolved oxygen; Forecasting; Forestry; Nitrates; Nitrogen oxides; Potable water; Reservoirs (water); Water quality; Water resources; Accurate modeling; Artificial neural network models; Correlation coefficient; Hydro-power generation; Industrial activities; Nitrate concentration; Nitrogen dioxides; Water quality parameters; Neural networksen_US
dc.description.abstractWater resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively. � 2020 WITPress. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18280/ijdne.150505
dc.identifier.epage652
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85096549010
dc.identifier.spage647
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85096549010&doi=10.18280%2fijdne.150505&partnerID=40&md5=0ca579cbfe39e006944264548fbd0d8f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25202
dc.identifier.volume15
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Design and Nature and Ecodynamics
dc.titleApplication of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwanen_US
dc.typeArticleen_US
dspace.entity.typePublication
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