Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models
Article
Article Title | Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models |
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ERA Journal ID | 864 |
Article Category | Article |
Authors | Deo, Ravinesh C. (Author), Samui, Pijush (Author) and Kim, Dookie (Author) |
Journal Title | Stochastic Environmental Research and Risk Assessment |
Journal Citation | 30 (6), pp. 1769-1784 |
Number of Pages | 16 |
Year | 2016 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1436-3240 |
1436-3259 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00477-015-1153-y |
Web Address (URL) | https://link.springer.com/article/10.1007/s00477-015-1153-y |
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. |
Keywords | prediction of evaporation; machine learning; relevance vector machine; extreme learning machine; multivariate adaptive regression spline |
ANZSRC Field of Research 2020 | 370201. Climate change processes |
300206. Agricultural spatial analysis and modelling | |
410404. Environmental management | |
460510. Recommender systems | |
490510. Stochastic analysis and modelling | |
370105. Atmospheric dynamics | |
469999. Other information and computing sciences not elsewhere classified | |
460207. Modelling and simulation | |
490199. Applied mathematics not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Vellore Institute of Technology, India | |
Kunsan National University, Korea | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q311w/estimation-of-monthly-evaporative-loss-using-relevance-vector-machine-extreme-learning-machine-and-multivariate-adaptive-regression-spline-models
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