Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
Article
Article Title | Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model |
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ERA Journal ID | 1956 |
Article Category | Article |
Authors | Deo, Ravinesh C. (Author), Kisi, Ogzur (Author) and Singh, Vijay P. (Author) |
Journal Title | Atmospheric Research |
Journal Citation | 184, pp. 149-175 |
Number of Pages | 27 |
Year | 2017 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0169-8095 |
1873-2895 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.atmosres.2016.10.004 |
Web Address (URL) | http://www.sciencedirect.com/science/article/pii/S0169809516304501 |
Abstract | Drought forecasting using standardized metrics of rainfall is a core task in hydrology and water resources management. Standardized Precipitation Index (SPI) is a rainfall-based metric that caters for different time-scales at which the drought occurs, and due to its standardization, is well-suited for forecasting drought at different periods in climatically diverse region. This study advances drought modelling using multivariate adaptive regression splines (MARS), least square support vector machine (LSSVM), and M5Tree models by forecasting SPI in eastern Australia. MARS model incorporated rainfall as mandatory predictor with month (periodicity), Southern Oscillation Index, Pacific Decadal Oscillation Index and Indian Ocean Dipole, ENSO Modoki and Nino 3.0, 3.4 and 4.0 data added gradually. The performance was evaluated with root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (r2). Best MARS model required different input combinations, where rainfall, sea surface temperature and periodicity were used for all stations, but ENSO Modoki and Pacific Decadal Oscillation indices were not required for Bathurst, Collarenebri and Yamba, and the Southern Oscillation Index was not required for Collarenebri. Inclusion of periodicity increased the r2 value by 0.5–8.1% and reduced RMSE by 3.0–178.5%. Comparisons showed that MARS superseded the performance of the other counterparts for three out of five stations with lower MAE by 15.0–73.9% and 7.3–42.2%, respectively. For the other stations, M5Tree was better than MARS/LSSVM with lower MAE by 13.8–13.4% and 25.7–52.2%, respectively, and for Bathurst, LSSVM yielded more accurate result. For droughts identified by SPI ≤ − 0.5, accurate forecasts were attained by MARS/M5Tree for Bathurst, Yamba and Peak Hill, whereas for Collarenebri and Barraba, M5Tree was better than LSSVM/MARS. Seasonal analysis revealed disparate results where MARS/M5Tree was better than LSSVM. The results highlight the importance of periodicity in drought forecasting and also ascertains that model accuracy scales with geographic/seasonal factors due to complexity of drought and its relationship with inputs and data attributes that can affect the evolution of drought events. |
Keywords | standardized precipitation index; drought forecasting; multivariate adaptive regression spline; least square support vector machine; M5Tree model |
ANZSRC Field of Research 2020 | 370201. Climate change processes |
410402. Environmental assessment and monitoring | |
469999. Other information and computing sciences not elsewhere classified | |
370901. Geomorphology and earth surface processes | |
410404. Environmental management | |
460207. Modelling and simulation | |
490109. Theoretical and applied mechanics | |
400513. Water resources engineering | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Canik Basari University, Turkiye | |
Texas A&M University, United States | |
Institution of Origin | University of Southern Queensland |
Funding source | Grant ID Academic Division Researcher Activation Incentive Scheme (RAIS; July– September 2015) grant |
Funding source | Grant ID Australian Government Endeavor Executive Fellowship (2015) |
https://research.usq.edu.au/item/q39zx/drought-forecasting-in-eastern-australia-using-multivariate-adaptive-regression-spline-least-square-support-vector-machine-and-m5tree-model
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