Modeling air concentration over macro roughness conditions by Artificial Intelligence techniques

8th International Conference on Future Environment and Energy

生态环境科学;能源学

Roshni, T.^1 ; Pagliara, S.^2

National Institute of Technology, Patna, Bihar

800005, India^1

University of Pisa, Via Gabba 22, Pisa

56122, Italy^2

关键词: Air concentrations;    Artificial intelligence techniques;    Hydraulic parameters;    Macro-roughness;    Model efficiency;    Potential capability;    Soft computing methods;    Training and testing;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/150/1/012002/pdf
DOI  :  10.1088/1755-1315/150/1/012002

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Aeration is improved in rivers by the turbulence created in the flow over macro and intermediate roughness conditions. Macro and intermediate roughness flow conditions are generated by flows over block ramps or rock chutes. The measurements are taken in uniform flow region. Efficacy of soft computing methods in modeling hydraulic parameters are not common so far. In this study, modeling efficiencies of MPMR model and FFNN model are found for estimating the air concentration over block ramps under macro roughness conditions. The experimental data are used for training and testing phases. Potential capability of MPMR and FFNN model in estimating air concentration are proved through this study.