Artificial neural network application in sediment prediction: a review
The development of an accurate sediment prediction model is a priority for all hydrological researchers. Several conventional approaches showed an inability to predict suspended sediment correctly. These approaches fall short in understanding the transport of sediments behaviour in rivers because of...
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| Format: | Book Section |
| Language: | en |
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Penerbit UTHM
2020
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| Online Access: | http://eprints.uthm.edu.my/2711/1/Ch03.pdf http://eprints.uthm.edu.my/2711/ |
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| _version_ | 1833417142479880192 |
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| author | Nda, Muhammad Adnan, Mohd Shalahuddin Mohd Yussof, Mohd Azlan Abdullahi Ahmad, Kabiru Isah, Nuhu Nda, Ramatu Muhammad |
| author2 | Ghadzali, Nor Syafiqah |
| author_facet | Ghadzali, Nor Syafiqah Nda, Muhammad Adnan, Mohd Shalahuddin Mohd Yussof, Mohd Azlan Abdullahi Ahmad, Kabiru Isah, Nuhu Nda, Ramatu Muhammad |
| author_sort | Nda, Muhammad |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The development of an accurate sediment prediction model is a priority for all hydrological researchers. Several conventional approaches showed an inability to predict suspended sediment correctly. These approaches fall short in understanding the transport of sediments behaviour in rivers because of its complex, non-stationary, and non-linear nature. There have been important advances in the theoretical understanding of Artificial Neural Network (ANN) over the last few decades, as well as algorithmic techniques for their implementation and applications of the approach to hydrological and practical problems. ANN and other machine learning models have been used in predicting complex non-linear relationships and patterns of large input parameters to achieve the desired output. This chapter reviews several relevant works of literature on sediment transport prediction while focusing on a wide range of ANN applications. ANN sediment transport models have increasingly attracted several researchers over the past years. Therefore, the need to acquire in-depth knowledge about their theory and modelling approaches. Besides, this chapter provides an overview of the ANN approach and other emerging machine learning hybrid models, which have yielded satisfactory results. Also, provided in this review are several examples of successful applicability of ANN in sediment prediction. |
| format | Book Section |
| id | my.uthm.eprints-2711 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2020 |
| publisher | Penerbit UTHM |
| record_format | eprints |
| spelling | my.uthm.eprints-27112021-11-29T03:18:36Z http://eprints.uthm.edu.my/2711/ Artificial neural network application in sediment prediction: a review Nda, Muhammad Adnan, Mohd Shalahuddin Mohd Yussof, Mohd Azlan Abdullahi Ahmad, Kabiru Isah, Nuhu Nda, Ramatu Muhammad TD Environmental technology. Sanitary engineering The development of an accurate sediment prediction model is a priority for all hydrological researchers. Several conventional approaches showed an inability to predict suspended sediment correctly. These approaches fall short in understanding the transport of sediments behaviour in rivers because of its complex, non-stationary, and non-linear nature. There have been important advances in the theoretical understanding of Artificial Neural Network (ANN) over the last few decades, as well as algorithmic techniques for their implementation and applications of the approach to hydrological and practical problems. ANN and other machine learning models have been used in predicting complex non-linear relationships and patterns of large input parameters to achieve the desired output. This chapter reviews several relevant works of literature on sediment transport prediction while focusing on a wide range of ANN applications. ANN sediment transport models have increasingly attracted several researchers over the past years. Therefore, the need to acquire in-depth knowledge about their theory and modelling approaches. Besides, this chapter provides an overview of the ANN approach and other emerging machine learning hybrid models, which have yielded satisfactory results. Also, provided in this review are several examples of successful applicability of ANN in sediment prediction. Penerbit UTHM Ghadzali, Nor Syafiqah Mohd. Salleh, Siti Nor Aishah Radin Mohamed, Radin Maya Saphira Hamdan, Rafidah 2020 Book Section PeerReviewed text en http://eprints.uthm.edu.my/2711/1/Ch03.pdf Nda, Muhammad and Adnan, Mohd Shalahuddin and Mohd Yussof, Mohd Azlan and Abdullahi Ahmad, Kabiru and Isah, Nuhu and Nda, Ramatu Muhammad (2020) Artificial neural network application in sediment prediction: a review. In: Water and Environmental Engineering. Penerbit UTHM, pp. 20-37. ISBN 978-967-2916-39-0 |
| spellingShingle | TD Environmental technology. Sanitary engineering Nda, Muhammad Adnan, Mohd Shalahuddin Mohd Yussof, Mohd Azlan Abdullahi Ahmad, Kabiru Isah, Nuhu Nda, Ramatu Muhammad Artificial neural network application in sediment prediction: a review |
| title | Artificial neural network application in sediment prediction: a review |
| title_full | Artificial neural network application in sediment prediction: a review |
| title_fullStr | Artificial neural network application in sediment prediction: a review |
| title_full_unstemmed | Artificial neural network application in sediment prediction: a review |
| title_short | Artificial neural network application in sediment prediction: a review |
| title_sort | artificial neural network application in sediment prediction: a review |
| topic | TD Environmental technology. Sanitary engineering |
| url | http://eprints.uthm.edu.my/2711/1/Ch03.pdf http://eprints.uthm.edu.my/2711/ |
| url_provider | http://eprints.uthm.edu.my/ |