Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models

Application of inclusive multiple model for the prediction of saffron water footprint

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 GMDHhoney badger algorithm (HBA), GMDHrat swarm algorithm (RSOA), GMDHsine cosine algorithm (SCA), and GMDHparticle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash– Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90%. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Laboratory study of the effects of terrestrial coastal forests on the absorption of solitary wave force

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models

Mahdi Asadi Aghbolaghi

Papers

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models