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Comparison and evaluation of the performance of data-driven models for estimating suspended sediment downstream of Doroodzan Dam
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Arash Jael *   |
| Assistant professor at the Agriculture department, Faculty of Engineering, Payam Noor University, Iran |
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Abstract: (3625 Views) |
| Dams control most of the sediment entering the reservoir by creating static environments. However, sediment leaving the dam depends on various factors such as dam management method, inlet sediment, water height in the reservoir, the shape of the reservoir, and discharge flow. In this research, the amount of suspended sediment of Doroodzan Dam based on a statistical period of 25 years has been investigated using three learning methods based on the data-driven algorithm, namely the K nearest neighbors, regression, and neural network. The results show that among different structures of the K nearest neighbors, the selection of 6 neighborhoods has more precise outcomes than other structures. Also, among different structures of neural networks, a structure with two hidden layers and 4 and 7 nodes in each hidden layer respectively, predicted suspended sediment more accurately than other neural network structures. Comparison of different algorisms was indicated that neural networks have more accurate results than other mentioned methods. |
Article number: 1 |
| Keywords: Artificial Neural Networks, Classic Regression, Doroodzan Dam, K-Nearest Neighbors, Suspended Sediment. |
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Full-Text [PDF 846 kb]
(801 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2021/07/1 | Revised: 2023/05/27 | Accepted: 2021/12/13 | ePublished: 2022/02/16
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