Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models

Deo, Ravinesh C. and Samui, Pijush and Kim, Dookie (2016) Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stochastic Environmental Research and Risk Assessment, 30 (6). pp. 1769-1784. ISSN 1436-3240

Abstract

The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T max), minimum temperature (T min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published 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: 14 Sep 2015 02:07
Last Modified: 20 Oct 2016 03:50
Uncontrolled Keywords: prediction of evaporation; machine learning; relevance vector machine; extreme learning machine; multivariate adaptive regression spline
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040102 Atmospheric Dynamics
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences
Identification Number or DOI: 10.1007/s00477-015-1153-y
URI: http://eprints.usq.edu.au/id/eprint/27693

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