When calculating uniform flows in open conduits and channels, Chezy’s resistance coefficient is not a problem data and its value is arbitrarily chosen. Such major disadvantage is met in all the geometric profiles of conduits and channels. Knowing the value of this coefficient is essential to both the design of the channel and normal depth calculation. The main objective of our research work is to focus upon the identification of the resistance coefficient relationship. On the basis of the rough model method (RMM) for the calculation of conduits and channels, a general explicit relation of the resistance coefficient in turbulent flow is established with different geometric profiles, particularly the egg-shaped conduit. Chezy’s resistance coefficient depends strongly on the filling rate, the discharge, the longitudinal slope, the absolute roughness of the internal walls of the conduit and the kinematic viscosity of the liquid. Moreover, in this work, a simplified method is presented to determine Chezy’s resistance coefficient with a limited number of data, namely the discharge, the slope of the conduit, the absolute roughness and the kinematic viscosity. Last but not least, after studying the variation of Chezy’s resistance coefficient as a function of the filling rate, an equally explicit expression is given for the easy calculation of this coefficient when its maximum value is reached. Examples of calculation are suggested in order to show how the Chezy’s coefficient can be calculated in the egg-shaped conduit.

In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases in Algeria. In the present study, public health database from the Algeria health ministry has been used to train and test the ELM models. The input parameters for the predictive models include Cumulative Confirmed COVID-19 Cases (CCCC), Calculated COVID-19 New Cases (CCNC), and Index Day (ID). The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912), and highest performances (NSE = 0.9999, IO = 0.9988, R2 = 0.9999). Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS-19 cases in Algeria.

The calculation of Chezy’s resistance coefficient (CRC) is typically not provided a priori in a design problem, and its value is often selected subjectively from the literature in most open channels and conduits for the uniform flow. The evaluation of this coefficient is crucial to channel design and for computing its normal depth. The primary purpose of this research study is to revisit the mathematical formulation for the resistance coefficient. A general explicit relation of the resistance coefficient in turbulent flow is set with different geometric profiles of conduits and channels, mainly the horseshoe-shaped tunnel using the rough model method (RMM). CRC is firmly based on the internal walls' absolute roughness of the channel, the liquid kinematic viscosity, the longitudinal slope, the discharge and the filling rate. Additionally, a simplified method is proposed to determine CRC with a restricted number of variables such as the kinematic viscosity, the absolute roughness, the slope of the conduit, and the discharge. Based on studying the variation of CRC as a function of the filling rate, another explicit expression is provided to compute this coefficient efficiently when its maximum value is reached. To demonstrate how Chezy’s resistance coefficient can be calculated in a horseshoe-shaped tunnel, some examples of calculations are proposed.