An overview of streamflow prediction using random forest algorithm
1 Department of Civil Engineering, Faculty of Engineering, Kano University of science and technology, KUST, Wudil, Nigeria.
2 Department of Civil Engineering, Faculty of Engineering Baze University, Abuja, Nigeria.
3 Department of Civil Engineering, Faculty of Engineering Bayero University, Kano, Nigeria.
4 Department of Civil Engineering, Federal University, Dutsin-ma, Katsina, Nigeria.
5 Department of Civil Engineering, Institute for Infrastructure & Environment, Heriot-Watt University, Edinburgh, UK.
6 Department of Electrical Engineering, Federal University Gusau, Nigeria.
7 Department of Information and Communication Technology (ICT), Yusuf Maitama Sule University Kano, Nigeria.
Review Article
GSC Advanced Research and Reviews, 2022, 13(01), 050–057.
Article DOI: 10.30574/gscarr.2022.13.1.0112
Publication history:
Received on 14 April 2022; revised on 06 October 2022; accepted on 09 October 2022
Abstract:
Since the first application of Artificial Intelligence in the field of hydrology, there has been a great deal of interest in exploring aspects of future enhancements to hydrology. This is evidenced by the increasing number of relevant publications published. Random forests (RF) are supervised machine learning algorithms that have lately gained popularity in water resource applications. It has been used in a variety of water resource research domains, including discharge simulation. Random forest could be an alternate approach to physical and conceptual hydrological models for large-scale hazard assessment in various catchments due to its inexpensive setup and operation costs. Existing applications, however, are usually limited to the implementation of Breiman's original algorithm for extrapolation and categorization issues, even though several developments could be useful in handling a variety of practical challenges in the water sector. In this section, we introduce RF and its variants for working water scientists, as well as examine related concepts and techniques that have earned less attention from the water science and hydrologic communities. In doing so, we examine RF applications in water resources, including streamflow prediction, emphasize the capability of the original algorithm and its extensions, and identify the level of RF exploitation in a variety of applications.
Keywords:
Artificial intelligent; Random Forest; Streamflow; Machine learning
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
