Support vector regression integrated with novel meta-heuristic algorithms for meteorological drought prediction

Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources

Anurag Malik; Yazid Tikhamarine; Doudja Souag-Gamane; Priya Rai; Saad Shauket Sammen; Ozgur Kisi

2021

Scholarcy highlights

  • Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources
  • The two-hybrid support vector regression–Grey Wolf Optimizer, and SVR–Spotted Hyena Optimizer models were constructed at Kumaon and Garhwal regions of Uttarakhand State
  • The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models
  • A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions
  • The results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms and Spotted Hyena Optimizer) for meteorological drought prediction in the study regions

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