Seyed Ahmad Hosseini; Ahmad Tabatabaei
Abstract
Introduction
Simulating suspended sediment in hydrological systems has always been challenging due to inherent complexities and uncertainties. This issue has led to the use of intelligent models such as Artificial Neural Networks (ANNs) as a suitable approach for predicting suspended sediment load. ...
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Introduction
Simulating suspended sediment in hydrological systems has always been challenging due to inherent complexities and uncertainties. This issue has led to the use of intelligent models such as Artificial Neural Networks (ANNs) as a suitable approach for predicting suspended sediment load. Therefore, the use of intelligent models like ANNs has expanded in this field. However, determining the optimal network structure (including the number of neurons, layers, weights, and biases) is usually done through trial and error, which is both time-consuming and inefficient. In this study, a multilayer perceptron neural network was used to simulate the daily suspended sediment load in the Qarasu Sarab watershed (Quri Chay and Hir Chai rivers) located in Ardabil province, Iran.
Materials and methods
In this research, an Artificial Neural Network (ANN) of the Multilayer Perceptron (MLP) type was utilized to simulate the daily suspended sediment load in the Sarab Qareh Su watershed (including the Quri Chay and Hir Chay rivers) in Ardabil province. The neural network models were trained not only whit the conventional backpropagation algorithm but also using the Particle Swarm Optimization (PSO) algorithm to optimize the weights and biases of the neurons. Furthermore, to increase the models' generalization capability, a Self-Organizing Map (SOM) clustering was employed. In addition to the backpropagation algorithm, the Particle Swarm Optimization (PSO) algorithm was also employed to optimize the network weights and biases. Furthermore, to enhance the model's generalization power, SOM clustering was used. The use of evolutionary algorithms such as PSO in training neural networks is an effective approach to improve the accuracy of intelligent models, especially in simulating river suspended sediment and applications related to water resources and watershed management structures.
Results and discussion
Using SOM clustering and the Davies-Bouldin index, the optimal number of clusters was determined as 12 for Koozeh Toupraqi station and 15 for Hir Chai station. Statistical analysis and the Kolmogorov-Smirnov (KS) test showed that data distributions across training, validation, and testing sets were similar, which enhances the generalization capability of the models. Training the neural network models with PSO yielded better performance and lower prediction errors compared to backpropagation. The ANN-PSO-Sig and ANN-PSO-Tan models achieved the best results at Koozeh Toupraqi and Hir Chai stations, respectively. Bias analysis further confirmed that PSO-trained models had lower errors in total sediment load estimation. Overall, results showed that PSO-based training outperforms pure backpropagation training. At Koozeh Toupraqi station, the hybrid ANN-PSO model with sigmoid activation function (ANN-PSO-Sig), and at Hir-Hirchai Topraghi station, the hybrid model with hyperbolic tangent activation function (ANN-PSO-Tan) were selected as optimal models, showing biases of +5.25% and -19.2% and RMSE values of 86.28 and 89.2 tons per day, respectively. These findings demonstrate the improvement in suspended sediment load prediction accuracy by using PSO in neural network training.
Conclusion
The use of the PSO metaheuristic algorithm in training neural network models improved their performance in simulating suspended sediment load. This method outperformed gradient-based error algorithms and provided more accurate weight optimization. The improved bias accuracy in PSO-trained models is crucial for designing hydraulic structures and water resource management. Furthermore, SOM clustering helped select homogeneous and representative datasets for model training, enhancing model generalizability. Overall, considering the complexities and uncertainties in hydrological systems, employing intelligent models combined with evolutionary optimization algorithms like PSO is an effective approach for simulating and monitoring suspended sediment loads. The obtained results can be applied in planning and implementing watershed engineering measures and water resource management.
Davood Nikkami; Reza Sokouti Oskuee; Ebrahim Brooshkeh; Faniz Heshmati
Abstract
Eroded soil particles within watershed areas are transported in three shape of dissolved, suspended and bed loads by river water discharge. Knowing the amount of suspended and bed loads are necessary in estimating sediment yield of watersheds. Usually, suspended load is measured daily in hydrometric ...
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Eroded soil particles within watershed areas are transported in three shape of dissolved, suspended and bed loads by river water discharge. Knowing the amount of suspended and bed loads are necessary in estimating sediment yield of watersheds. Usually, suspended load is measured daily in hydrometric stations, while this does not happen for bed load. Although, there are many differences between geology formations, rainfall intensities and water discharges of watersheds, the amount of bed loads are considered as a constant unique coefficient of suspended load. So, there would be highly errors in watersheds’ sediment load estimates. In this research, after collecting bed and suspended load data of three hydrometric stations of Badlan on Alandchay, Pol Yazdakan on Ghotourchay and Mazraeh on Baroonchay Rivers in Western Azerbaijan, the data were analyzed and best relations were fitted between bed load and suspended loads. According to statistical analysis all data had high coefficient of correlation at the level of 99 percent and the best equations were chosen accordingly. The ratio of bed to suspended load in Yazdakan, Badlan and Mazraeh stations were 79, 49 and 13 percent, respectively. This ratio was 47 percent for all three stations.
Soheyla Aghabeigi; Abdol Rasoul Telvari; Sayed Khalagh Mirnia; Sadat Feiznia; Mehdi Vafakhah
Abstract
Sediment concentration in rivers, especially in seasonal ones, is affected by flood situation due to changes of rainfall or snowmelt events. Due to the importance of flooding flows in inundation and the useful age of reservoirs built on seasonal rivers, the study and assessment of this issue seems to ...
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Sediment concentration in rivers, especially in seasonal ones, is affected by flood situation due to changes of rainfall or snowmelt events. Due to the importance of flooding flows in inundation and the useful age of reservoirs built on seasonal rivers, the study and assessment of this issue seems to be essential. In the present research, suspended sediment concentration variation in spring and autumn floods and runoff from snowmelt in spring, have been studied for Abshine River in Ekbatan Dam Watershed. Over the forecast period, three storms in autumn, two storms in spring and five snow melt events were selected and compared. Analyzing 226 suspended load samples showed its variability in the area. Sediment rating curves in different base times showed different trends. The correlation coefficient (r) were 0.79 and 0.50 for storms and snowmelts and 0.81 for all data, respectively. Also, coefficient and power value analysis of sediment rating curve, presented the same erodibility for hill slope in both seasons (spring and autumn). Separating falling and rising limbs of flow hydrograph and assessing their effect on discharge explains a better Q-sediment concentration relationship. The hysteretic shape of discharge and sediment concentration had clockwise and anticlockwise form and compound pattern of both forms that reflected the distribution of probable sediment sources throughout the catchment.
