论文标题
应用数据处理方法和新优化算法的应用，以预测植被覆盖的沉积物传输速率
Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover
论文作者
论文摘要
种植植被是降低沉积物转移率的实用解决方案之一。植被覆盖的增加会降低环境污染和沉积物的运输速率（STR）。由于沉积物和植被相互作用复杂，预测沉积物的传输速率具有挑战性。这项研究旨在使用新的和优化的数据处理方法（GMDH）的新版本（GMDH）预测植被覆盖的沉积物传输速率。此外，这项研究介绍了一种用于预测沉积物传输速率的新集合模型。模型输入包括波高，波速，密度覆盖，波力，D50，植被盖的高度和盖茎直径。独立的GMDH型号和优化的GMDH模型，包括GMDH蜂蜜badger算法（HBA）GMDH大鼠群群算法（RSOA）VGMDH正弦余弦算法（SCA）和GMDH粒子swarm swarm优化（GMDH-PSO），用于预测沉积速度。作为下一步，使用独立的GMDH的输出来构建集合模型。合奏模型的MAE为0.145 m3/s，而GMDH-HBA，GMDH-RSOA，GMDH-SCA，GMDH-PSOA和GMDH的MAE在0.176 M3/s中为0.176 M3/s，0.312 M3/s，0.367 M3/s，0.367 M3/s，0.367 M3/s，0.498 M3/s，和0.498 m3/s，和0.498 m3/s，和0.61 M3/s，和0.61 M3/s，和0.61 M3/s，和0.498 m3/s，和0.61 M3/s，和0.61 M3/s，和0.61 M3/s，和0.498 m3/s。集合模型的Nash Sutcliffe系数（NSE），GMDH-HBA，GMDH-RSOA，GMDH-SCA，GMDH-PSOA和GHMDH分别为0.95 0.95 0.93、0.89、0.86、0.86、0.82和0.76。此外，这项研究表明，植被覆盖的沉积物运输速率降低了90％。结果表明，集合和GMDH-HBA模型可以准确预测沉积物的传输速率。根据这项研究的结果，可以使用IMM和GMDH-HBA监测沉积物的传输速率。这些结果对于在大盆地管理和规划水资源很有用。
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 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 percent. 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.
