Linear and non-linear approaches to predict the Darcy-Weisbach friction factor of overland flow using the extreme learning machine approach

In modeling of overland flow and erosion, the overland flow friction factor (f), is a crucial factor. Due to the importance of a good understanding of f and its variability, the current study aimed to investigate the capability of non-linear approaches to estimate the Darcy-Weisbach friction factor of overland flow and its components (sediment transport, wave, form, and grain friction factors) through the Extreme Learning Machine (ELM) approach. Four datasets were used herein which were obtained from flume experiments done by different researchers. In order to investigate the effects of different parameters on the friction factor, numerous models consisting of various parameters were utilized to predict the friction factor using the ELM approach. The modeling procedure was established in two stages; the first stage aimed to model the overland flow friction factor and investigate the effect of the different parameters on the friction factor using non-linear separation via the ELM approach. In the second stage, the friction factor was linearly separated into different types of friction factors and then the separate components were estimated. Sensitivity analysis results confirmed the key role of Froude number (Fr) values for most of the models. On the other hand, the results obtained for estimated values of the friction factor were acceptable and outperformed available empirical approaches.