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Longitudinal Dispersion(LD) is the dominant process of scalar transport innatural streams. An accurate prediction on LD coefficient(Dl) can produce aperformance leap in related simulation. The emerging machine learning(ML)techniques provide a self-adaptive tool for this problem. However, most of theexisting studies utilize an unproved quaternion feature set, obtained throughsimple theoretical deduction. Few studies have put attention on its reliabilityand rationality. Besides, due to the lack of comparative comparison, the properchoice of ML models in different scenarios still remains unknown. In thisstudy, the Feature Gradient selector was first adopted to distill the localoptimal feature sets directly from multivariable data. Then, a global optimalfeature set (the channel width, the flow velocity, the channel slope and thecross sectional area) was proposed through numerical comparison of thedistilled local optimums in performance with representative ML models. Thechannel slope is identified to be the key parameter for the prediction of LDC.Further, we designed a weighted evaluation metric which enables comprehensivemodel comparison. With the simple linear model as the baseline, a benchmark ofsingle and ensemble learning models was provided. Advantages and disadvantagesof the methods involved were also discussed. Results show that the supportvector machine has significantly better performance than other models. Decisiontree is not suitable for this problem due to poor generalization ability.Notably, simple models show superiority over complicated model on thislow-dimensional problem, for their better balance between regression andgeneralization.

A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient