Prediction of SPEI using MLR and ANN: a case study for Wilsons Promontory Station in Victoria

Mouatadid, Soukayna and Deo, Ravinesh C. and Adamowski, Jan F. (2015) Prediction of SPEI using MLR and ANN: a case study for Wilsons Promontory Station in Victoria. In: 2015 IEEE 14th International Conference on Machine Learning and Applications, 9-11 Dec 2015, Miami, Florida, USA.

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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher. Dr R C Deo acknowledges support from University of Southern Queensland Academic Division 'Research Activation Incentive Scheme (RAIS)' grant 2015.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 15 Mar 2016 04:54
Last Modified: 27 Jun 2016 01:48
Uncontrolled Keywords: multi-linear regression model; artificial neural network model; standardized precipitation index; drought modelling
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
04 Earth Sciences > 0401 Atmospheric Sciences > 040102 Atmospheric Dynamics
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences
Identification Number or DOI: 10.1109/ICMLA.2015.87
URI: http://eprints.usq.edu.au/id/eprint/28977

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