Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction
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ORIGINAL PAPER
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction Anurag Malik1
•
Yazid Tikhamarine2 • Doudja Souag-Gamane2 • Ozgur Kisi3,4 • Quoc Bao Pham4,5
Accepted: 31 August 2020 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Accurate and reliable prediction of streamflow is vital to the optimization of water resources management, reservoir flood operations, catchment, and urban water management. In this research, support vector regression (SVR) was optimized by six meta-heuristic algorithms, namely, Ant Lion Optimization (SVR-ALO), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Harris Hawks Optimization (SVR-HHO), Particle Swarm Optimization (SVR-PSO), and Bayesian Optimization (SVR-BO) to predict daily streamflow in Naula watershed, State of Uttarakhand, India. The significant inputs and parameter combinations for hybrid SVR models were extracted through Gamma Test before processing. The results obtained by hybrid SVR models during calibration (training) and validation (testing) periods, which were compared against observed streamflow using performance indicators of root mean square error (RMSE), scatter index (SI), coefficient of correlation (COC), Willmott index (WI), and by visual inspection (time-series plot, scatter plot and Taylor diagram). The results of comparison demonstrated that SVR-HHO during calibration/validation periods (RMSE = 92.038/181.306 m3/s, SI = 0.401/0.715, COC = 0.881/0.717, and WI = 0.928/0.777) had superior performance to the SVR-ALO, SVR-MVO, SVR-SHO, SVR-PSO, and SVR-BO models in predicting daily streamflow in the study basin. In addition, the new HHO algorithm outperformed the other meta-heuristic algorithms in terms of prediction accuracy. Keywords Meta-heuristic algorithms Streamflow Gamma test Naula watershed Uttarakhand
1 Introduction Streamflow process is the key component of the hydrological cycle, which is complex, and hard to predict accurately (Cheng et al. 2016; Neto et al. 2018). It has been & Anurag Malik [email protected] 1
Punjab Agricultural University, Regional Research Station, Bathinda-151001, Punjab, India
2
Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32, Bab Ezzouar, Algiers, Algeria
3
Department of Civil Engineering, Ilia State University, Tbilisi, Georgia
4
Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
5
Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang 550000, Vietnam
largely affected by several parameters, namely precipitation, temperature, evapotranspiration, and characteristics of land use and drainage basin (Adnan et al. 2019). The reliable and precise prediction of streamflow process has great importance in designing, planning, optimizing, utilizing, and management of water resources (Adnan et al. 2018; Roy and Singh 2019). Streamflow prediction models are generally classified in two broad ca
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