Comparison of improved relevance vector machines for streamflow predictions

Abstract

This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Various lagged values of hydroclimatic data (e.g., precipitation, temperature, and streamflow) are used as inputs to the models. The relevance vector machine tuned with dwarf mongoose optimization algorithm improved the efficiency of single method in monthly streamflow prediction. It is found that the integrating metaheuristic algorithms into single relevance vector machine improves the prediction efficiency, and among the input combinations, the lagged streamflow data are found to be the most effective variable on current streamflow whereas precipitation has the least effect.