Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

Ehab Gomaa; Bilel Zerouali; Salah Difi; Khaled A El-Nagdy; Celso Augusto Guimarães Santos; Zaki Abda; Sherif S M Ghoneim; Nadjem Bailek; Richarde Marques da Silva; Jitendra Rajput; Enas Ali

doi:10.1016/j.heliyon.2023.e18819

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

Heliyon. 2023 Jul 30;9(8):e18819. doi: 10.1016/j.heliyon.2023.e18819. eCollection 2023 Aug.

Authors

Affiliations

¹ Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
² Vegetal Chemistry-Water-Energy Research Laboratory, Faculty of Civil Engineering and Architecture, Department of Hydraulic, Hassiba Benbouali, University of Chlef, B.P. 78C, Ouled Fares, Chlef, 02180, Algeria.
³ Department of Civil Engineering, College of Engineering, Taif University, P.O. BOX 11099, Taif, 21944, Saudi Arabia.
⁴ Department of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900, João Pessoa, Paraíba, Brazil.
⁵ Research Laboratory of Water Resources, Soil, And Environment, Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Amar Telidji University, P.O. Box 37.G, 03000, Laghouat, Algeria.
⁶ Electrical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
⁷ Laboratory of Mathematics Modeling and Applications, Department of Mathematics and Computer Science, Faculty of Sciences and Technology, Ahmed Draia University of Adrar, Adrar, 01000, Algeria.
⁸ Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Tamanghasset, Algeria.
⁹ MEU Research Unit, Middle East University, Amman, Jordan.
¹⁰ Department of Geosciences, Federal University of Paraíba, 58051-900, João Pessoa, Paraíba, Brazil.
¹¹ Water Technology Center, ICAR-IARI, New Delhi, 110012, India.
¹² Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt.

Abstract

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Keywords: Brazilian river basin; Empirical mode decomposition (EMD); Floods; Hydrological simulation; Particle swarm optimization (PSO); Water resource management.