Open Access

ARTICLE

Applications of Advanced Optimized Neuro Fuzzy Models for Enhancing Daily Suspended Sediment Load Prediction

Rana Muhammad Adnan^1,2, Mo Wang^1,*, Adil Masood³, Ozgur Kisi^4,5,6,*, Shamsuddin Shahid⁷, Mohammad Zounemat-Kermani⁸

1 College of Architecture and Urban Planning, Guangzhou University, Guangzhou, 510006, China
2 Center for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Chennai, 600001, India
3 Department of Natural and Applied Sciences, TERI School of Advanced Studies, New Delhi, 110070, India
4 Department of Civil Engineering, Lübeck University of Applied Science, Lübeck, 23562, Germany
5 Department of Civil Engineering, School of Technology, Ilia State University, Tbilisi, 0162, Georgia
6 School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, Republic of Korea
7 School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia
8 Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, 7616914111, Iran

* Corresponding Authors: Mo Wang. Email: ; Ozgur Kisi. Email:

(This article belongs to the Special Issue: Soft Computing Applications of Civil Engineering including AI-based Optimization and Prediction)

Computer Modeling in Engineering & Sciences 2025, 143(1), 1249-1272. https://doi.org/10.32604/cmes.2025.062339

Received 16 December 2024; Accepted 20 March 2025; Issue published 11 April 2025

Abstract

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 (R²). 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 R², 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.

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