Assessment of GEP and ANN for Predicting Suspended Sediment Load: A Case Study of Ghatoor and Aland Rivers, West Azerbaijan, Iran

Estimation of the volume of suspended sediment load of rivers, especially when dam constructed on it, is one of the tremendous challenges that civil engineers faced. It is crucial to accurately predict the suspended sediment load to effectively mitigate the negative consequences of this phenomenon. To estimate the total suspended sediment accumulated behind the Aland and Ghatoor dams, two models of artificial intelligence, Gene Expression Programming (GEP) and Artificial Neural Network (ANN), were employed in this study. The performance of these two AI models compared with the traditional method, Sediment Rating Curve (SRC), for estimating the suspended sediment volume using hydrometric stations from ۱۹۶۹ to ۲۰۱۷. Unfortunately, the appropriate data from ۲۰۱۷ to the present is not available from authorities of the West Azerbaijan province, so inevitably, we used the hydrologic records till the end of the year ۲۰۱۷ in this article. Two statistical indices were used to evaluate the models: the coefficient of determination (R-squared) and the Mean Absolute Error (MAE). Based on these indices, the intelligent models performed better than the SRC in estimating the suspended sediment volume. In comparing the GEP and ANN models, the performance criteria show that the ANN model produces better results. For the Ghatoor River, the performance indicators of the ANN model were MAE=۹۹۳.۱ ton/day and R^۲=۰.۹۱۰, which is ۴۵% and ۴۳% higher than the GEP model and SRC method, respectively. For Aland River, the performance indicators of the ANN model were MAE=۵۱۹.۲ ton/day and R^۲=۰.۹۶۱, which is ۱۲% and ۵۷% higher than the GEP model and SRC method, respectively. In conclusion, for predicting the suspended sediment load in Ghatoor and Aland Rivers, the ANN model can be the best choice for this purpose.

کلیدواژه ها:

Artificial Neural Network ، Gene expression programming ، Suspended Sediment Load ، Sediment Rating Curve