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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, SWe 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.
History
Publication title
Proceedings of the Machine Learning for Sensory Data Analysis 2014Editors
A Rahman, JD Deng, J LuiPagination
62-66ISBN
978-1-4503-3159-3Department/School
School of Information and Communication TechnologyPublisher
The Association for Computing MachineryPlace of publication
New YorkEvent title
The 2nd Workshop on Machine Learning for Sensory Data Analysis 2014Event Venue
Gold Coast, QLDDate of Event (Start Date)
2014-12-02Date of Event (End Date)
2014-12-02Rights 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 onlyRepository Status
- Restricted