Streamflow Data Infilling Using Machine Learning Techniques with Gamma Test

Length, completeness, and quality of hydrological time-series can affect considerably the efficiency of decisions in water resources engineering. Regrettably, obtaining short, incomplete, and low-quality data is not rare. In this study, different machine learning techniques have been implemented and applied to fill in missed data of streamflow at Coxs River, in Australia. The implemented techniques are Support Vector Regression improved by Equilibrium Optimizer (SVR-EO) and Particle Swarm Optimizer (SVR-PSO), alongside Artificial Neural Networks trained by EO (ANN-EO) and PSO (ANN-PSO). Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) have been used for comparison purposes. Rainfall data provided by five climatic stations located near Coxs River along with Kowmung River streamflow records have been used to fill the gaps in the Coxs River time-series. The gamma test has been used to select the convenient data combination that reduces errors in prediction models. According to the findings, SVR-PSO and SVR-EO outperformed the other techniques with \(R^{2}\approx 0.94\) for training, and \(R^{2}\approx 0.85\) for testing part. The imputation process and the developed SVR-EO and SVR-PSO could be applied to other rivers in different countries to ensure whether these methods could be generalized.