Machine learning: An efficient alternative to the variable infiltration capacity model for an accurate simulation of runoff rates

The present study aims to investigate the performance of the artificial intelligence to emulate the conventional physically-based hydrological models. Although these conventional models could accurately depict the underlying physical processes, but they require a lengthy preprocessing phase as well as a tedious calibration time. Therefore, a need to examine the potential efficient alternative for these models is highly felt. This need becomes imperative once we adopt fine temporal and spatial resolutions for our hydrological modeling, leading to a massive number of to-be-analyzed cells. To this end, we propose a learning framework towards an accurate prediction of runoff rates using meteorological variables, and hence, mimicking the Variable Infiltration Capacity (VIC) by a nimble systematized predictive model. We also present a novel strategy to optimally select the most informative subset of data to train our predictive model, out of the pool of accessible data. This strategy would then considerably enhance the performance of our prediction in terms of computation time. We reported our result as the Pearson correlation coefficient between the predicted and actual runoff rates. Our predictive model was able to forecast the runoff rates with the mean correlation coefficient of 0.9007 for the cells within the study basin.