Multiple Resolution River flow Time Series Modelling using Machine Learning Methods

Abstract

We develop multiple resolution river flow time series model using machine learning methods at three locations in South Australia. In multiple resolution river flow models, we identify the best method from a set of widely used machine learning methods. We also identify optimized lag values of river flow and rainfall as input time series in predicting river flow multiple days ahead. The best models are ranked based on mean absolute error. Experimental results demonstrate that M5P method provides the lowest error over artificial neural network, linear regression and support vector regression in modelling river flow for multiple days ahead prediction for all three locations. Although M5P gives better accuracy over other methods for these locations as also found in the recent research in hydrological time series modelling, it may not be the best method for other geographical locations. Detailed evaluation of statistical and machine learning methods may be needed in predicting river flow for any location of interest.