Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Alfeu D Martinho; Henrique S Hippert; Leonardo Goliatt

doi:10.1038/s41598-023-41113-5

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Sci Rep. 2023 Aug 24;13(1):13824. doi: 10.1038/s41598-023-41113-5.

Authors

Alfeu D Martinho¹, Henrique S Hippert^#², Leonardo Goliatt^#³

Affiliations

¹ Exact Sciences and Technology Department, Púnguè University, Tete Delegation, Campus Universitário de Cambinde-EN106, Matundo, Tete, Mozambique. alfeudiasm@gmail.com.
² Statistics Department, Federal University of Juiz de Fora, Campus Universitário, Rua José Lourenço Kelmer, s/n-São Pedro, Juiz de Fora, Minas Gerais, Brazil.
³ Computational and Applied Mechanics Department, Federal University of Juiz de Fora, Campus Universitário, Rua José Lourenço Kelmer, s/n-São Pedro, Juiz de Fora, Minas Gerais, Brazil.

^# Contributed equally.

Abstract

Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.