An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery
Correlation 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...
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2023
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my.uniten.dspace-258902023-05-29T17:05:26Z An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery Aziz A. Essam Y. Ahmed A.N. Huang Y.F. El-Shafie A. 57205233815 57203146903 57214837520 55807263900 16068189400 Correlation 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 algorithms Sediment 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 AUTHORS Final 2023-05-29T09:05:26Z 2023-05-29T09:05:26Z 2021 Article 10.1016/j.asej.2021.03.014 2-s2.0-85106315415 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106315415&doi=10.1016%2fj.asej.2021.03.014&partnerID=40&md5=f1a4e2c883c468f1046acc8c9efe03a3 https://irepository.uniten.edu.my/handle/123456789/25890 12 4 3429 3438 All Open Access, Gold Ain Shams University Scopus |
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Correlation 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 algorithms |
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57205233815 |
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57205233815 Aziz A. Essam Y. Ahmed A.N. Huang Y.F. El-Shafie A. |
| format |
Article |
| author |
Aziz A. Essam Y. Ahmed A.N. Huang Y.F. El-Shafie A. |
| spellingShingle |
Aziz A. Essam Y. Ahmed A.N. Huang Y.F. El-Shafie A. An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery |
| author_sort |
Aziz A. |
| title |
An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery |
| title_short |
An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery |
| title_full |
An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery |
| title_fullStr |
An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery |
| title_full_unstemmed |
An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery |
| title_sort |
assessment of sedimentation in terengganu river, malaysia using satellite imagery |
| publisher |
Ain Shams University |
| publishDate |
2023 |
| _version_ |
1806428439513661440 |
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13.222552 |