فهرست نشریات علمی دسترسی آزاد ایران

Predicting suspended sediment load is important for water resource management, water quality protection, erosion control, infrastructure planning, flood management, ecological conservation, pollution control, and environmental impact assessments. As a novel aspect of this study, the ANFIS-M5T model is introduced and used to predict suspended sediment loads. Our study combines the adaptive neuro-fuzzy inference system (ANFIS) and the M5T model to create a hybrid model for predicting suspended sediment load (SSL). The lagged rainfall, discharge, and SSL values were used to predict SSL. The results showed that the introduced ANFIS-M5T model performs better than other models so that it had the lowest mean absolute error (MAE: 525), the highest Nash Sutcliffe efficiency (0.98) and the lowest Percent bias (4).The ANFIS model had the second lowest MAE of 576, followed by MLP (586), RFN (682), and M5T (981). Thus the ANFIS-M5T was a reliable tool for predicting SSL. By tackling the obstacles, assessing various methods, and showcasing the ANFIS-M5T model's efficiency, our research contributes to the continuous improvement and advancement of sediment measurement techniques and tools.