eCite Digital Repository

Multiple resolution river flow time series modelling using machine learning methods


Shahriar, MS and Kamruzzaman, M and Beecham, S, Multiple resolution river flow time series modelling using machine learning methods, Proceedings of the Machine Learning for Sensory Data Analysis 2014, 2 December 2014, Gold Coast, QLD, pp. 62-66. ISBN 978-1-4503-3159-3 (2014) [Refereed Conference Paper]

Not available

Copyright Statement

Copyright 2014 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only

DOI: doi:10.1145/2689746.2689755


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.

Item Details

Item Type:Refereed Conference Paper
Keywords:environmental and hydrological sensor data, machine learning, river flow modelling, time series analysis
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Modelling and simulation
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Shahriar, MS (Dr Sumon Shahriar)
ID Code:118052
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2017-07-03
Last Modified:2017-10-17

Repository Staff Only: item control page