Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin, Malaysia
| Autor: | Yuk Feng Huang, Majid Mirzaei, Kit Fai Fung, Chai Hoon Koo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Atmospheric Science
Global and Planetary Change geography geography.geographical_feature_category 010504 meteorology & atmospheric sciences business.industry Computer science 0208 environmental biotechnology Drainage basin 02 engineering and technology Management Monitoring Policy and Law 01 natural sciences 020801 environmental engineering Downstream (manufacturing) Agriculture business Water resource management 0105 earth and related environmental sciences Water Science and Technology |
| Zdroj: | Journal of Water and Climate Change. 11:1383-1398 |
| ISSN: | 2408-9354 2040-2244 |
| Popis: | Drought is a harmful and little understood natural hazard. Effective drought prediction is vital for sustainable agricultural activities and water resources management. The support vector regression (SVR) model and two of its enhanced variants, namely, fuzzy-support vector regression (F-SVR) and boosted-support vector regression (BS-SVR) models, for predicting the standardized precipitation evapotranspiration indices (SPEI) (in this case, SPEI-1, SPEI-3 and SPEI-6, at various timescales) with a lead time of one month, were developed to minimize potential drought impact on oil palm plantations at the downstream end of the Langat River Basin, which has a tropical climate pattern. Observed SPEIs from periods 1976 to 2011 and 2012 to 2015 were used for model training and validation, respectively. By applying the MAE, RMSE, MBE and R2 as model assessments, it was found that the F-SVR model was best with the trend of improving accuracy when the timescale of the SPEIs increased. It was also found that differences in model performance deteriorates with increased timescale of the SPEIs. The outlier reducing effect from the fuzzy concept has better improvement for the SVR-based models compared to the boosting technique in predicting SPEI-1, SPEI-3 and SPEI-6 for a one-month lead time at the downstream of Langat River Basin. |
| Databáze: | OpenAIRE |
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