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