An interdependent evolutionary machine learning model applied to global horizontal irradiance modeling

Basílio, Samuel da Costa Alves; Saporetti, Camila Martins; Goliatt, Leonardo

doi:10.1007/s00521-023-08342-1

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Various evolutionary search algorithms, such as genetic algorithms (GA), firefly algorithm (FFA), particle swarm optimization (PSO), salp swarm algorithm (SSA), gray wolf optimization (GWO), spotted hyena optimizer (SHO), differential evolution (DE), cuckoo search algorithm (CSA), Ant colony optimization (ACO), and even multi-objective optimization design (MOOD), have been proposed. These algorithms have demonstrated excellent global optimum search capabilities compared to classic optimization methods, leading to the development of hybrid models 3 – 15 . The application of these hybrid approaches for predicting hydrological variables is a relatively new technique that has shown significant improvement in forecasting 16 – 24 .…”

Section: Introductionmentioning

confidence: 99%

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

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

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

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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Development of hybrid computational data-intelligence model for flowing bottom-hole pressure of oil wells: New strategy for oil reservoir management and monitoring

Goliatt

Mohammad

Abba

et al. 2023

Fuel

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Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

et al. 2023

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This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, τ 1 -τ 2 ) and 3 outputs (acceleration, a x -a y -a z ) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (τ 1 -τ 2 ) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (a x -a y -a z ) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The 1st NN model is trained with 3 inputs (a x -a y -a z ) and 2 targets (τ 1 -τ 2 ) training data. The 2nd NN model is trained with 6 inputs (amplitudes and phases of a x -a y -a z ) and 2 targets (τ 1 -τ 2 ) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The 3rd NN model is trained with 6 inputs (amplitudes and phases of a x -a y -a z ) and 4 targets (amplitudes and phases of τ 1 -τ 2 ) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analyzed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the 3rd NN, the actuator signals (τ 1 -τ 2 ) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of τ 1 -τ 2 ). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.

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An interdependent evolutionary machine learning model applied to global horizontal irradiance modeling

Cited by 3 publications

References 39 publications

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

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

Development of hybrid computational data-intelligence model for flowing bottom-hole pressure of oil wells: New strategy for oil reservoir management and monitoring

Feature Extraction and NN-Based Enhanced Test Maneuver Deployment for 2 DoF Vehicle Simulator

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