Author
Mahdi Asadi Aghbolaghi
Bio: Mahdi Asadi Aghbolaghi is an academic researcher from Shahrekord University. The author has contributed to research in topics: Sediment transport. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Topics: Sediment transport
Papers
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TL;DR: In this paper, the effects of long waves as simulated solitary waves on sediment transport to estimate the sediment transport rate in different coastal forest cover (CFC) densities were studied.
6 citations
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01 Jan 2015
TL;DR: In this article, the authors discuss the effect of the environment on the performance of the construction process and propose a method to improve the quality of the work performed by the construction company.
Abstract: 河床上的沉没植被的存在能改变水流动结构并且改变沉积运动的状态。在这研究，沉积的早期的运动在一个实验室实验面对在开的隧道的沉没灵活植被被调查。植被与柱体在平行数组安排了的灵活橡胶被模仿。植被密度，水深度，和早期的运动上的沉积谷物尺寸的效果被调查。试验性的结果显示沉积的早期的运动速度作为植被密度减少和水深度和沉积谷物尺寸增加增加。与灵活植物，沉积的早期的运动速度比它没有植被的低，并且比它与僵硬植被的大。一个一般早期的运动速度方程被导出，它能被用于灵活、僵硬的植被条件。
10 citations
5 citations
TL;DR: In this article , three experimental forest management types were considered: the sparse type (ST), the middle type (MT), and the dense type (DT), where Ry is the relative yield index.
3 citations
TL;DR: In this article , the impact of concrete grade control structures (CGCSs) on geomorphology change and fish habitat has been evaluated from the perspective of the spatial-averaged occurrence probability of sweep events near the bed and flow diversity.
1 citations
TL;DR: In this paper , a new ensemble model was introduced to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH) for predicting 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 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.