Forecast of total flows for medium-term horizon via data-driven modeling
DOI:
https://doi.org/10.5902/2179460X87856Keywords:
Inflow forecast, Machine learning, Weka, Reservoir operationAbstract
This study presents the application of artificial neural networks (ANN), k-nearest neighbors algorithm (KNN), and support vector regression (SVR) for modeling the prediction of inflows to the Sobradinho reservoir in Bahia, Brazil. Using the Weka software, six formulations were tested for 3-month forecasts, with data divided into cross-validation and calibration-validation schemes. Efficiency was evaluated using the Nash-Sutcliffe coefficient, highlighting the superiority of SVR. The inclusion of attributes such as average flow and precipitation improved efficiencies. The model using KNN with 13 neighbors was incorporated into an enhanced implicit stochastic optimization strategy for the operation of the reservoir. This model was compared to other operational methods, showing superiority in vulnerability index.
Downloads
References
Akbari, M., Overloop, P. J. v., & Afshar, A. (2010). Clustered k nearest neighbor algorithm for daily inflow forecasting. Water Resources Management, 25(5), 1341–1357.
Ávila, L., Mine, M. R. M., & Kaviski, E. (2020). Probabilistic long-term reservoir operation employing copulas and implicit stochastic optimization. Stochastic Environmental Research and Risk Assessment, 34(7), 931–947.
Celeste, A. B. & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429–1443.
Chiamsathit, C., Adeloye, A. J., & Bankaru-Swamy, S. (2016). Inflow forecasting using artificial neural networks for reservoir operation. Proceedings of the International Association of Hydrological Sciences, 373, 209–214.
Giuliani, M., Lamontagne, J. R., Reed, P. M., & Castelletti, A. (2021). A state-of-the-art review of optimal reservoir control for managing conflicting demands in a changing world. Water Resources Research, 57(12).
Khadr, M. & Schlenkhoff, A. (2021). GA-based implicit stochastic optimization and RNN-based simulation for deriving multi-objective reservoir hedging rules. Environmental Science and Pollution Research, 28(15), 19107–19120.
Maddu, R., Pradhan, I., Ahmadisharaf, E., Singh, S. K., & Shaik, R. (2022). Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information. Journal of Hydrology, 612, 128153.
Morettin, P. A. & Singer, J. M. S. (2023). Estatística e Ciência de Dados. LTC, Rio de Janeiro.
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.
Nagy, I. V., Asante-Duah, K., & Zsuffa, I. (2002). Hydrological Dimensioning and Operation of Reservoirs: Practical Design Concepts and Principles. Springer Netherlands.
Nash, J. & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I – A discussion of principles. Journal of Hydrology, 10(3), 282–290.
Refsgaard, J. C. (1990). Terminology, modelling protocol and classification of hydrological model codes. In Abbott, M. B. & Refsgaard, J. C., editors, Distributed Hydrological Modelling, pages 17–39. Springer Netherlands.
Santana, R. F. & Celeste, A. B. (2021). Stochastic reservoir operation with data-driven modeling and inflow forecasting. Journal of Applied Water Engineering and Research, 10(3), 212–223.
Shu, X., Ding, W., Peng, Y., Wang, Z., Wu, J., & Li, M. (2021). Monthly streamflow forecasting using convolutional neural network. Water Resources Management, 35(15), 5089–5104.
Solomatine, D. P. (2006). Data-driven modeling and computational intelligence methods in hydrology. In Anderson, M. G. & McDonnell, J. J., editors, Encyclopedia of Hydrological Sciences. Wiley.
Solomatine, D. P., Maskey, M., & Shrestha, D. L. (2007). Instance-based learning compared to other data-driven methods in hydrological forecasting. Hydrological Processes, 22(2), 275–287.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam.
Wu, M.-C., Lin, G.-F., & Lin, H.-Y. (2012). Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map. Hydrological Processes, 28(2), 386–397.
Wu, W., Eamen, L., Dandy, G., Razavi, S., Kuczera, G., & Maier, H. R. (2023). Beyond engineering: A review of reservoir management through the lens of wickedness, competing objectives and uncertainty. Environmental Modelling & Software, 167, 105777.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ciência e Natura
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To access the DECLARATION AND TRANSFER OF COPYRIGHT AUTHOR’S DECLARATION AND COPYRIGHT LICENSE click here.
Ethical Guidelines for Journal Publication
The Ciência e Natura journal is committed to ensuring ethics in publication and quality of articles.
Conformance to standards of ethical behavior is therefore expected of all parties involved: Authors, Editors, Reviewers, and the Publisher.
In particular,
Authors: Authors should present an objective discussion of the significance of research work as well as sufficient detail and references to permit others to replicate the experiments. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. Review Articles should also be objective, comprehensive, and accurate accounts of the state of the art. The Authors should ensure that their work is entirely original works, and if the work and/or words of others have been used, this has been appropriately acknowledged. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Authors should not submit articles describing essentially the same research to more than one journal. The corresponding Author should ensure that there is a full consensus of all Co-authors in approving the final version of the paper and its submission for publication.
Editors: Editors should evaluate manuscripts exclusively on the basis of their academic merit. An Editor must not use unpublished information in the editor's own research without the express written consent of the Author. Editors should take reasonable responsive measures when ethical complaints have been presented concerning a submitted manuscript or published paper.
Reviewers: Any manuscripts received for review must be treated as confidential documents. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should be conducted objectively, and observations should be formulated clearly with supporting arguments, so that Authors can use them for improving the paper. Any selected Reviewer who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the Editor and excuse himself from the review process. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.
