Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics

Belotti, Jônatas Trabuco; Castanho, Diego Solak; Araujo, Lilian N.; Silva, Lucas V. da; Alves, Thiago Antonini; Tadano, Yara S.; Stevan, Sérgio Luiz; Corrêa, Fernanda Cristina; Siqueira, Hugo Valadares

doi:10.1016/j.envres.2020.110106

Cited by 31 publications

(15 citation statements)

References 34 publications

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Section: Autoregressive Model -Armentioning

confidence: 99%

“…The Autoregressive model of order p -AR (p) is part of the set of methods described by Box & Jenkins (Box et al, 2008) for stationary series. Its extensive use stems from its efficiency and relative ease in mathematical treatment (Box et al, 2008;Siqueira et al, 2018). The Equation that defines the AR(p) model is described in Equation ( 3 ϕ i = 1, ..., p, are adjustable coefficients, and the term at is the random component or the error inherent in the forecasting process.…”

Section: Autoregressive Model -Armentioning

confidence: 99%

“…As can be noticed in Equation ( 3), the model does not have feedback on outgoing responses. Therefore, its optimal coefficients, towards a minimum mean square error (MSE), can be obtained analytically through the Yule-Walker equations (Box et al, 2008), presented in Equation ( 4):…”

Section: Autoregressive Model -Armentioning

confidence: 99%

“…The monthly case is intended for short-term and medium-term planning (Maceira et al, 2002;2004). The Monthly Program of Operation coordinated by ONS, the most used methodology for prediction is based on linear models of the Box & Jenkins family (Box et al, 2008), highlighting the Periodic Autoregressive (PAR) (CEPEL, 2020). In this case, 12 Autoregressive (AR) models are adjusted independently, and each one is optimized to provide the data for each of the 12 months of the year (Siqueira et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Belotti

Mendes

Leme

et al. 2021

Journal of Hydrology and Hydromechanics

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Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.

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Section: Autoregressive Model -Armentioning

confidence: 99%

Section: Autoregressive Model -Armentioning

confidence: 99%

Section: Autoregressive Model -Armentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Belotti

Mendes

Leme

et al. 2021

Journal of Hydrology and Hydromechanics

Self Cite

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show abstract

“…When dealing with optimization problems, the agents in metaheuristics roam in the search space to obtain good solutions using experimental and local information [15]. The choice of which one performs better depends on the application problem [2,5,9,12,16]. The objective of this work is to analyze the behavior of three bio-inspired optimization algorithms for searching for optimal solutions for a multimodal problem as represented by the Gaussian Adaptive Proportional, Integral and Derivative (GAPID) control strategy applied to typical power supplies of medical equipment with the Buck converter topology.…”

Section: Introductionmentioning

confidence: 99%

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

et al. 2021

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This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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Integrating Reinforcement Learning and Optimization Task: Evaluating an Agent to Dynamically Select PSO Communication Topology

Lira

Macedo

Siqueira

et al. 2023

Lecture Notes in Computer Science

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Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics

Cited by 31 publications

References 34 publications

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Integrating Reinforcement Learning and Optimization Task: Evaluating an Agent to Dynamically Select PSO Communication Topology

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