Application of Three Intelligent Models in Estimation of Watersheds Suspended Load (Case Study: Dareh-Roud Watershed, Ardabil Province)

Document Type : Research Article

Authors

• Y. Ataie ¹
• M. R. Nikpour ²
• A. Kanooni ¹
• Y. Hoseini ¹

¹ University of Mohaghegh Ardabili

² University of Mohaghegh Ardabili, Ardabil, Iran

10.22067/jsw.v34i4.86251

Abstract

Introduction: Suspended load estimation is utilized to study and investigate many problems of water engineering sciences such as dam reservoir design, transportation of sediments and pollution in the rivers, creation of stable channels, estimation of erosion and sedimentation around bridge piers, and watershed management. The purpose of this study was to estimate the suspended load in the Dareh-Roud watershed in Ardabil province using the rivers discharge values and the physiographic characteristics of the sub-basins. Moreover, annual suspended load and sediment specific discharge were calculated for the whole of the watershed.
Materials and Methods: In this study, the Dareh-Roud watershed in Ardebil province was considered as the study area. The flow discharge and suspended load data were collected from 16 hydrometric stations with a statistical period of 15 years from 2001-2015. The physiographic characteristics of sub-basins, including area (A), slope (S), shape factor (Sf), and curve number (CN), were achieved using ArcGIS and WMS. Five different input combinations were defined based on the effect of flow discharge variables and physiographic properties on the suspended load. Also, considering the area and slope parameters, the sub-basins were divided into two groups (i.e., the first and second groups). The performance of data-intelligent models, including Artificial Neural Networks (ANN), Adaptive Neural-Fuzzy Interference System (ANFIS), and Gene Expression Programming (GEP) models were investigated in the predict of the suspended load in the study area. Several statistical indicators, including determination coefficient (R2), root mean square error (RMSE), and Nash- Sutcliffe efficiency (NS), were utilized to evaluate the model’s efficiency.
Results and Discussion: According to the results, estimation of suspended load without using the physiographic characteristics resulted in a high error, and in contrast, the suspended load estimation was most accurate by using a combined scenario involving all physiographic aspects and flow discharge. The scatterplots indicated that in the first group, the points were concentrated around the 1:1 axis for the values of less than 20 (ton/day). However, for the greater amounts, the scattering of issues around the one-to-one line was not appropriate, which means that the models were in the condition of underestimation. Similar conditions were observed for the second group, the excellent dispersion was seen for the values of less than 1000 (ton/day), and in general, the models had underestimation conditions. However, in both groups, the dispersion of the GEP model was somewhat better than the other models. Based on the values of R2 and NS, ANN and ANFIS models had the acceptable and satisfactory accuracy for the first group. The GEP model was more reliable and efficient in estimating the suspended load of the first group. On the other hand, the efficiency of ANN and ANFIS was not acceptable for the second group. Comparison of the results of different models using the best input combination indicated that the GEP model with the highest determination coefficient (R2 = 0.68), the lowest root mean square error (RMSE = 7.69 ton/day). The NS equal to 0.55 in the validation step has shown better performance than the other models in estimating the suspended load for the first group. Similarly, for the second group, the GEP model with the highest determination coefficient (R2 = 0.72), the lowest root means square error (RMSE = 975.26 ton/day). The NS equal to 0.43 in the validation step has shown better performance than other models in estimating the suspended load.
Conclusion: In the present study, the efficiency of different intelligent models was investigated in the suspended load estimation of Dareh-roud watershed. In this regard, an extended period (i.e., during 15 years) of measured data, including flow discharge and sediment at the hydrometric stations located on the mentioned watershed, were used. In order to simulate the suspended load, five different input combinations were considered. For all models, the accuracy of suspended load estimation was improved by combining the physiographic characteristics and discharge values. Due to the higher accuracy of the GEP model, regional sediment models were achieved for the first and second groups, separately. Also, annual suspended load and sediment specific discharge were calculated for all sub-basins. According to the results, most of the suspended load of the Dareh-Roud watershed is produced and transported in its old rivers (i.e., Dareh-Roud and Qarah-Su). Based on the results of this research, in the Dareh-Roud watershed, 6.33 million tons of suspended sediments were transported during 2001-2015.

Keywords

• Suspended Load
• Dareh-Roud watershed
• Intelligent models
• Specific sediment discharge

20.1001.1.20084757.1399.34.4.6.0

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