University Of Tasmania

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Multiple resolution river flow time series modelling using machine learning methods

conference contribution
posted on 2023-05-23, 12:12 authored by Shahriar, MS, Kamruzzaman, M, Beecham, S
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.


Publication title

Proceedings of the Machine Learning for Sensory Data Analysis 2014


A Rahman, JD Deng, J Lui






School of Information and Communication Technology


The Association for Computing Machinery

Place of publication

New York

Event title

The 2nd Workshop on Machine Learning for Sensory Data Analysis 2014

Event Venue

Gold Coast, QLD

Date of Event (Start Date)


Date of Event (End Date)


Rights 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

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified