Runoff Estimation by Machine Learning Methods and Application to the Euphrates Basin in Turkey

Abstract

Machine learning (ML) techniques have been popular data-driven approaches for hydrological studies during the last few decades owing to their capability to identify complex nonlinear relationships between input and output data without the requirement for physical understanding of the system. This paper aims to predict river flows using various ML methods [feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), and genetic programming (GP)] and also a non-ML method (multiple linear regression) in the Euphrates Basin in Turkey. Infilling the missing data in the runoff record of the selected stations in Euphrates Basin is also an objective of this study. The ML methods were applied to the three main sub-basins of the Euphrates Basin, namely the Upper, Middle, and Lower Euphrates Basins. ANFIS and FFNN methods were the most successful ML methods for runoff estimation in the Upper and Lower Euphrates Basins, whereas GP and ANFIS models were the best ones in the Middle Euphrates Basin. Missing flow data were constructed successfully in the selected stations.