TY  - EJOU
AU  - Adnan, Rana Muhammad 
AU  - Wang, Mo 
AU  - Masood, Adil 
AU  - Kisi, Ozgur 
AU  - Shahid, Shamsuddin 
AU  - Zounemat-Kermani, Mohammad 

TI  - Applications of Advanced Optimized Neuro Fuzzy Models for Enhancing Daily Suspended Sediment Load Prediction
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 1
SN  - 1526-1506

AB  - Accurate daily suspended sediment load (SSL) prediction is essential for sustainable water resource management, sediment control, and environmental planning. However, SSL prediction is highly complex due to its nonlinear and dynamic nature, making traditional empirical models inadequate. This study proposes a novel hybrid approach, integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Gradient-Based Optimizer (GBO), to enhance SSL forecasting accuracy. The research compares the performance of ANFIS-GBO with three alternative models: standard ANFIS, ANFIS with Particle Swarm Optimization (ANFIS-PSO), and ANFIS with Grey Wolf Optimization (ANFIS-GWO). Historical SSL and streamflow data from the Bailong River Basin, China, are used to train and validate the models. The input selection process is optimized using the Multivariate Adaptive Regression Splines (MARS) method. Model performance is evaluated using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Nash Sutcliffe Efficiency (NSE), and Determination Coefficient (R<sup>2</sup>). Additionally, visual assessments, including scatter plots, Taylor diagrams, and violin plots, provide further insights into model reliability. The results indicate that including historical SSL data improves predictive accuracy, with ANFIS-GBO outperforming the other models. ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R<sup>2</sup>, demonstrating its superior learning ability and adaptability. The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling. Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.
KW  - Suspended sediment load; prediction; neuro-fuzzy; gradient-based optimizer; ANFIS

DO  - 10.32604/cmes.2025.062339