Prediction of SPEI using MLR and ANN: a case study for Wilsons Promontory Station in Victoria
Paper
Paper/Presentation Title | Prediction of SPEI using MLR and ANN: a case study for Wilsons Promontory Station in Victoria |
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Presentation Type | Paper |
Authors | Mouatadid, Soukayna (Author), Deo, Ravinesh C. (Author) and Adamowski, Jan F. (Author) |
Editors | Guerrero, Juan E. |
Journal or Proceedings Title | Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications |
ERA Conference ID | 43445 |
Number of Pages | 7 |
Year | 2015 |
Place of Publication | United States |
ISBN | 9781509002870 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICMLA.2015.87 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/7424328 |
Conference/Event | 2015 IEEE 14th International Conference on Machine Learning and Applications |
International Conference on Machine Learning and Applications | |
Event Details | International Conference on Machine Learning and Applications ICMLA Rank C C C C C C C C C |
Event Details | 2015 IEEE 14th International Conference on Machine Learning and Applications Event Date 09 to end of 11 Dec 2015 Event Location Miami, United States of America |
Abstract | The prediction of drought is of major importance in climate-related studies, hydrologic engineering, wildlife or agricultural studies. This study explores the ability of two machine learning methods to predict 1, 3, 6 and 12 months standardized precipitation and evapotranspiration index (SPEI) for the Wilsons Promontory station in eastern Australia. The two methods are multiple linear regression (MLR) and artificial neural networks (ANN). The data-driven models were based on combinations of the input variables: mean precipitations, mean, maximum and minimum temperatures and evapotranspiration, for data between 1915 and 2012. Two performance metrics were used to compare the performance of the optimum MLR and ANN models: the coefficient of determination (R2) and the root mean square error (RMSE). It was found that ANN provided greater accuracy than MLR in forecasting the 1, 3, 6 and 12 months SPEI. |
Keywords | multi-linear regression model; artificial neural network model; standardized precipitation index; drought modelling |
ANZSRC Field of Research 2020 | 370201. Climate change processes |
370105. Atmospheric dynamics | |
370202. Climatology | |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | McGill University, Canada |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q366z/prediction-of-spei-using-mlr-and-ann-a-case-study-for-wilsons-promontory-station-in-victoria
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