Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover
Golnaz Mirzakhani, Elham Ghanbari-Adivi, Rohollah Fattahi, Mohammad, Ehteram, Amir Mosavi, Ali Najah Ahmed, Ahmed El-Shafieg

TL;DR
This study develops and compares advanced GMDH-based models, including ensemble and optimized versions, to accurately predict sediment transport rates under vegetation cover, aiding water resource management.
Contribution
Introduces new optimized GMDH models and an ensemble approach for predicting sediment transport rates with high accuracy under vegetation cover.
Findings
Ensemble model achieved MAE of 0.145 m3/s and NSE of 0.95.
Vegetation cover reduced sediment transport rate by 90%.
GMDH-HBA model accurately predicts sediment transport rates.
Abstract
Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH honey badger algorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm (SCA), and GMDH particle swarm optimization (GMDH-PSO), were used…
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Taxonomy
TopicsSoil erosion and sediment transport · Hydrology and Watershed Management Studies · Hydrology and Sediment Transport Processes
MethodsMasked autoencoder
