Long-lead streamflow forecasting using computational intelligence methods while considering uncertainty issue

While some robust artificial intelligence (AI) techniques such as Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) have been frequently employed in the field of water resources, documents aimed to explore their uncertainty levels are few and far between. Meanwhile, uncertainty determination of these AI models in practical applications is highly important especially when we aimed to use the AI models for streamflow forecast due to the repercussions of poorly managed water resources. With the aid of a global daily streamflow dataset, understanding the uncertainty of GEP, MT, and MARS for forecasting streamflow of natural rivers was studied. The efficiency of uncertainty analysis was quantified by two statistical indicators: 95% Percent Prediction Uncertainty (95%PPU) and R-factor. The results demonstrated that MT had lower uncertainty (95%PPU=0.59 and R-factor=1.67) in comparison with MARS (95%PPU=0.61 and R-factor=1.92) and GEP (95%PPU=0.64 and R-factor=2.03). Overall, although the confidence interval bands of uncertainty for the AI models almost captured the mean streamflow measurements, wide bands of uncertainty were obtained and consequently remarkable uncertainty in the calculation of monthly streamflow values was met.