Publication:
An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery

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dc.citedby1
dc.contributor.authorAziz A.en_US
dc.contributor.authorEssam Y.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57205233815en_US
dc.contributor.authorid57203146903en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:26Z
dc.date.available2023-05-29T09:05:26Z
dc.date.issued2021
dc.descriptionCorrelation methods; Deposition; Forecasting; Hierarchical clustering; Image analysis; Infrared devices; K-means clustering; Machine learning; Nanostructured materials; Rivers; Satellite imagery; Sediments; Correlation analysis; Correlation coefficient; Ground truth data; Preparation process; River environment; Sediment deposition; United states geological surveys; Unsupervised machine learning; Learning algorithmsen_US
dc.description.abstractSediment deposition causes the reduction of aquatic habitats and increase of water velocities within rivers, which negatively impacts the environment and the surrounding ecology. This makes the prediction of river sediment deposition a key factor for the protection of river environments. The prediction of sediment deposition in rivers through the integration of satellite imagery and unsupervised machine learning is beneficial and convenient, as it is less resource-intensive due to not requiring ground-truth data. The Terengganu River in Malaysia is used as a case study in this research. This study aims to discuss satellite imagery's key preparation processes, namely image correction and identification of determinant image bands through a correlation analysis. Satellite imagery of the Terengganu River between 1989 and 2019 is obtained from the United States Geological Survey (USGS). Image correction is successfully implemented on the available satellite imagery with the results shown in this study. Through the performed correlation analysis, the study finds that the determinant image bands for river sediment deposition prediction using unsupervised machine learning are the NST spectral bands, which consist of the NIR, SWIR, and TIR bands. This is due to the NST spectral bands exhibiting low correlations with respect to the RGB bands. It is found that correlation coefficients between the NIR band and red, green, and blue bands are generally the lowest, especially in 2009 with values of 0.1087, 0.2085, and 0.1252, respectively. This indicates that the NIR band is the most important determinant image band in predicting river sediment deposition. This study also identifies k-means, clustering large application (Clara), and hierarchical agglomerative clustering (HAC) as suitable unsupervised machine learning algorithms to be utilized in predicting river sediment deposition. Studies on the application of unsupervised machine learning algorithms on satellite imagery in the field of river sediment deposition prediction are currently scarce, possibly due to the gap of knowledge on the initial steps required for such application. Therefore, this study's novelty is the introduction and discussion on critical preliminary processes, specifically image correction and identification of determinant image bands, that are required for the successful implementation of unsupervised machine learning algorithms on satellite imagery for the prediction of river sediment deposition. � 2021 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asej.2021.03.014
dc.identifier.epage3438
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85106315415
dc.identifier.spage3429
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85106315415&doi=10.1016%2fj.asej.2021.03.014&partnerID=40&md5=f1a4e2c883c468f1046acc8c9efe03a3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25890
dc.identifier.volume12
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.titleAn assessment of sedimentation in Terengganu River, Malaysia using satellite imageryen_US
dc.typeArticleen_US
dspace.entity.typePublication
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