A machine learning-based approach to predict the velocity profiles in small streams

This article addresses the determination of velocity profile in small streams by employing powerful machine learning algorithms that include artificial neural networks (ANNs), support vector machine (SVMs), and k-nearest neighbor algorithms (k-NN). Therefore, this study also aims to present a reliable and low-cost method for predicting velocity profile. The data set used in this study was achieved by field measurements performed by using the acoustic Doppler velocimeter (ADV) between 2005 and 2010, in Central Turkey. The eight observational variables and calculated non-dimensional parameters were used as inputs to the models for predicting the target values, u (point velocity in measured verticals). Performances of prediction methods were determined via 10-fold cross-validation approach. The comparative results revealed that k-NN algorithms outperformed the other two machine learning models, with the R value of 0.98 ± 0.0069 and the MAE value of 0.053 ± 0.0075, while ANNs and SVMs models have the R values of 0.95 ± 0.0085 and 0.89 ± 0.0046, the MAE values of 0.085 ± 0.0077 and 0.099 ± 0.0117, respectively. Importance of the predictor variables for ANNs and SVMs models were also presented by using sensitivity analysis.