Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, Gaussian process, and minimax probability machine regression: case study of Brisbane City

Deo, Ravinesh C. and Samui, Pijush (2017) Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, Gaussian process, and minimax probability machine regression: case study of Brisbane City. Journal of Hydrologic Engineering, 22 (6). pp. 1-15. ISSN 1084-0699

Abstract

Daily evaporative loss (EpEp) forecasting models are decisive tools with potential applications in hydrology, the design of water systems, urban water assessments, and irrigation management. This paper performs a case study for forecasting daily EpEp for Brisbane city using least-square support-vector regression (LSSVR). A limited set of predictor data with solar radiation and exposure, maximum/minimum temperatures, wind speed, and precipitation (March 1, 2014 to March 31, 2015) is adopted to develop the predictive model. The results are evaluated with Gaussian process regression (GPR), minimax probability machine regression (MPMR), and genetic programming (GP) models. In the testing phase, a correlation coefficient of 0.895 is attained between the observed and forecasted EpEp by LSSVR that contrasted 0.875 (GPR), 0.864 (MPMR), and 0.628 (GP). A sensitivity test of predictor variables shows that approximately 28.5% of features are extracted from solar radiation data with 18.1% (wind speed), 16.6% (precipitation), and 10–15% (minimum and maximum temperature). The root-mean square error for LSSVR is lower than the GPR, MPMR, and GP models by 16.2, 11.4, and 79.4%, and the cumulative frequency of forecasting error attained for LSSVR is the highest within the smallest error band. The results confirm the better utility of LSSVR in relation to GP, GPR, and MPMR models for forecasting daily evaporative loss.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: The research project was supported by the USQ Academic Development and Outside Studies Program (ADOSP; July 2016 – January 2017) grant awarded to Dr RC Deo to establish research collaboration with Dr Pijush Samui, NIT Patna, India). Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 01 Mar 2017 06:02
Last Modified: 04 May 2018 01:22
Uncontrolled Keywords: evaporation model; least-square support-vector regression; minimax probability machine regression; genetic programming; Gaussian process regression
Fields of Research : 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
04 Earth Sciences > 0401 Atmospheric Sciences > 040103 Atmospheric Radiation
01 Mathematical Sciences > 0102 Applied Mathematics > 010204 Dynamical Systems in Applications
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
Identification Number or DOI: 10.1061/(ASCE)HE.1943-5584.0001506
URI: http://eprints.usq.edu.au/id/eprint/30866

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