Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms

• Published in: Environmental science and pollution research international Vol. 28; no. 29; pp. 39139 - 39158
• Main Authors: Malik, Anurag, Tikhamarine, Yazid, Sammen, Saad Shauket, Abba, Sani Isah, Shahid, Shamsuddin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; autocorrelation; Autocorrelation functions; Crop damage; Drought; Drought index; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental monitoring; Environmental science; hawks; India; Inspection; Natural disasters; Optimization; Particle swarm optimization; prediction; Prediction models; Rain; Rainfall; regression analysis; Regression models; Research Article; Root-mean-square errors; Statistical analysis; Support vector machines; Waste Water Technology; Water damage; Water Management; Water Pollution Control; Water resources; Water resources management; Water scarcity; water shortages; India; PACF; Effective drought index; Harris Hawks optimization; Uttarakhand; Particle swarm optimization
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-13445-0

Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535–0.965, MAE = 0.363–0.622, NSE = 0.558–0.860, COC = 0.760–0.930, and WI = 0.862–0.959) outperformed the SVR-PSO model (RMSE = 0.546–0.967, MAE = 0.372–0.625, NSE = 0.556–0.855, COC = 0.758–0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.