IJESD 2019 Vol.10(9): 288-293 ISSN: 2010-0264
doi: 10.18178/ijesd.2019.10.9.1189
doi: 10.18178/ijesd.2019.10.9.1189
Machine Learning: An Efficient Alternative to the Variable Infiltration Capacity Model for an Accurate Simulation of Runoff Rates
Hamidreza Ghasemi Damavandi, Dimitrios Stampoulis, Reepal Shah, Yuhang Wei, Dragan Boscovic, and John Sabo
Abstract—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.
Index Terms—Active learning, artificial intelligence, random forests, variable infiltration capacity.
Hamidreza Ghasemi Damavandi, Dimitrios Stampoulis, Reepal Shah, Yuhang Wei, and John Sabo are with Future H2O, the Office of Knowledge Enterprise Develepment, Arizona State University, Tempe, AZ, USA (e-mail: {hghasemi, dstampou, Reepal.Shah, John.L.Sabo}@asu.edu, hhuweiyuhang@163.com).
Dragan Boscovic is with the Center of Assured and Scalable Data Engineering (CASCADE), Arizona State University, Tempe, AZ, USA (e-mail: dboscovi@asu.edu).
Index Terms—Active learning, artificial intelligence, random forests, variable infiltration capacity.
Hamidreza Ghasemi Damavandi, Dimitrios Stampoulis, Reepal Shah, Yuhang Wei, and John Sabo are with Future H2O, the Office of Knowledge Enterprise Develepment, Arizona State University, Tempe, AZ, USA (e-mail: {hghasemi, dstampou, Reepal.Shah, John.L.Sabo}@asu.edu, hhuweiyuhang@163.com).
Dragan Boscovic is with the Center of Assured and Scalable Data Engineering (CASCADE), Arizona State University, Tempe, AZ, USA (e-mail: dboscovi@asu.edu).
Cite: Hamidreza Ghasemi Damavandi, Dimitrios Stampoulis, Reepal Shah, Yuhang Wei, Dragan Boscovic, and John Sabo, "Machine Learning: An Efficient Alternative to the Variable Infiltration Capacity Model for an Accurate Simulation of Runoff Rates," International Journal of Environmental Science and Development vol. 10, no. 9, pp. 288-293, 2019.
Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).General Information
- ISSN: 2010-0264 (Print); 2972-3698 (Online)
- Abbreviated Title: Int. J. Environ. Sci. Dev.
- Frequency: Bimonthly
- DOI: 10.18178/IJESD
- Editor-in-Chief: Prof. Richard Haynes
- Managing Editor: Ms. Cherry L. Chen
- Indexing: Scopus (CiteScore 2022: 1.4), Google Scholar, CNKI, ProQuest, EBSCO, etc.
- E-mail: ijesd@ejournal.net
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