Outlier robust extreme learning machine to simulate discharge coefficient of side slots

Applied Water Science

Farzad Hasani¹  Saeid Shabanlou¹ 

[1] Department of Water Engineering, Kermanshah Branch, Islamic Azad University;

关键词: Side slots;    Discharge coefficient;    Outlier robust extreme learning machine;    Uncertainty analysis, Partial derivative sensitivity analysis;    Sensitivity analysis;   

DOI  :  10.1007/s13201-022-01687-3

来源: DOAJ

【 摘 要 】

Abstract As the first time, this paper attempts to recreate the discharge coefficient (DC) of side slots by another artificial intelligence procedure named "Outlier Robust Extreme Learning Machine (ORELM)". Accordingly, at first, the variables affecting the DC comprising the ratios of the flow depth to the side slot length (Y m /L), the side slot crest elevation to the side slot length (W/L), the main channel width to the side slot length (B/L), as well as the Froude number (F r) are determined and subsequently five ORELM models (ORELM 1 to ORELM 5) are created utilizing these variables. From that point forward, laboratory measurements are arranged into two datasets comprising training (70%) and testing (30%). At the subsequent stage, the best model alongside the most affecting input variables is presented by executing a sensitivity examination. The most impressive model (i.e., ORELM 3) reproduces DC values as far as B/L, W/L and F r. It is worth focusing on that ORELM 3 forecasts DC values with worthy precision. For instance, the correlation coefficient (R), the scatter index (SI) and the Nash–Sutcliffe effectiveness (NSC) for ORELM 3 are acquired in the examination state to be 0.936, 0.049 and 0.852, independently. Examining the outcomes yielded from the simulation demonstrates that W/L and F r are the most impacting factors to reproduce the DC. Besides, the findings of the sensitivity examination uncover that ORELM 3 acts in an underestimated way. Finally, a computer code is put forward to compute the DC of side slots.

【 授权许可】

Unknown