International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 4 (April 2023), Pages: 114-120

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 Original Research Paper

 Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India

 Author(s): 

 Shashi Shankar Ojha *, Vivekanand Singh, Thendiyath Roshni

 Affiliation(s):

 Department of Civil Engineering, National Institute of Technology, Patna, Bihar 800005, India

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3289-1018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.04.014

 Abstract:

Machine learning (ML) models have emerged as potential methods for rainfall-runoff modeling in recent decades. The appeal of ML models for such applications is owing to their competitive performance when compared to alternative approaches, ease of application, and lack of rigorous distributional assumptions, among other attributes. Despite the promising results, most ML models for rainfall-runoff applications have been limited to areas where rainfall is the primary source of runoff. The potential of Random Forest (RF), a popular ML method, for rainfall-runoff prediction in the Punpun river basin, India, is investigated in this paper. The correlation coefficient (R), Root mean squared error (RMSE), Mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE) are four statistical metrics used to compare RF performance to that of alternative ML models. Model evaluation metrics indicate that RF outperforms all others. In the RF model, we got the best NSE score of 0.795. These findings offer new perspectives on how to apply RF-based rainfall-runoff modeling effectively.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Machine learning, Random forest, Rainfall-runoff modeling

 Article History: Received 22 June 2022, Received in revised form 13 October 2022, Accepted 18 January 2023

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Ojha SS, Singh V, and Roshni T (2023). Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India. International Journal of Advanced and Applied Sciences, 10(4): 114-120

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 

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