Predicting Suspended Sediment using a Hybrid Model of Sediment Rating Curve and Artificial Neural Network in the Naroun Afjeh Station

Introduction and Goal
The concentration of suspended sediments is one of the most important water quality indicators in surface water resources and a significant hydrological phenomenon. However, traditional methods, such as sediment rating curves (SRCs), lack accuracy due to not considering all effective parameters. In this context, hybrid models that include SRCs and artificial neural networks (ANNs) have emerged as a promising approach for enhancing SSC prediction accuracy. These models, with their ability to use complex nonlinear patterns, outperform traditional methods. This study aims to develop and implement an SRC-ANN hybrid model for SSC prediction. The proposed model is predicted to significantly improve prediction accuracy by combining the strengths of both methods, aiding in optimal water resource management and the proper functioning of hydraulic structures.
Materials and Methods
This research introduces a novel hybrid model that integrates sediment rating curves (SRCs) and artificial neural networks (ANNs) was used for a more accuracy prediction of suspended sediment in the Naroun (Afejeh) hydrometric station. For this purpose, data from 222 sample of flow discharge and suspended sediment over a 50-years period (1971 to 2021) were used. Additionally, 14 different were employed, including 6 sediment rating curve methods, 6 artificial neural network methods and 2 hybrid methods, to simulate suspended sediment. The performance of each method was evaluated using statistical criteria such as coefficient of determination (R²), efficiency coefficient (ME), and mean relative error percentage (RME).
Results and discussion
The results showed that among the sediment rating curve methods, the most accurate simulation of the observed sediment discharge conditions compared to other methods was related to the midpoint method, with a coefficient of determination (R²) of 0.840, a modeling efficiency (ME) of 0.820, and a mean relative error (RME) of 0.211%. Also, among the artificial neural network methods, the most accurate simulation was related to the CANFIS method, with a modeling efficiency (ME) of 0.8123 and a mean relative error (RME) of 0.248. Finally, to improve the prediction results, hybrid models 1 and 2 were used. The results showed that he best estimate of suspended sediment was related to hybrid method 1, with a modeling efficiency (ME) of 0.8761 and a mean relative error (RME) of 0.06359%. In the mentioned method, both the estimation of peak flow discharge and the estimation of base flow discharge were very accurate, and it was introduced as the most accurate method for predicting suspended sediments. These results highlight the potential of using hybrid model 1 to significantly improve prediction accuracy and to better fit the observed data.
Conclusion and Suggestions
Among the sediment rating curve methods, the mean category sediment rating curve method was identified as the best approach for predicting suspended sediment due to its consideration of data distribution and flexibility. The performance of artificial neural networks (ANNs) in simulating sediment for base and normal flows was good, but were weaker in predicting sediment during flood events. The most accurate method for suspended sediment prediction is the Hybrid model 1, which use a MSM and ANN methods. Improper selection of a sediment prediction method can lead to inaccurate results. Also, it is essential to examine the impact of other variables beyond flow discharge on sediment. The results of this research showed that it is possible to significantly increase the accuracy of suspended sediment prediction using hybrid models, and that these models can be utilized as an effective tool for managing and predicting suspended sediments, as well as improving water resource management. It is recommended to use advanced sampling facilities and a larger number of samples at hydrometric stations, especially in high-flow and flood conditions, for the development and optimization of hybrid methods. Additionally, it is suggested that engineers and water resource managers utilize the findings of this research to develop optimal strategies for suspended sediment management.