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Mr. Jônatas Belotti
Universidade Estadual de Campinas - UNICAMP

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0 Machine Learning
0 Neural Networks
0 Optimization
0 Optimization Algorithms
0 Forecasting Air pollution

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Article
Published: 23 November 2020 in International Transactions in Operational Research
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Time series forecasting problems are often addressed using linear techniques, especially the autoregressive (AR) models, due to their simplicity combined with good performances. It is possible to generalize a linear predictor by allowing infinite impulse response (IIR) through the addition of feedback loops, as occurs in the autoregressive and moving average (ARMA) models and IIR filters. However, the calculation of the free coefficients of these structures is more complex, as the optimization problem has no closed‐form solution. This work conducts an extensive investigation on the use of linear models to predict monthly seasonal streamflow series associated with Brazilian hydroelectric plants. The main goal is to reach the best achievable performance with linear approaches. We propose the application of recursive models, estimating their parameters with the aid of bioinspired metaheuristics: particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and two immune‐inspired algorithms, the CLONALG, and the artificial immune network for optimization (Opt‐aiNet). The AR model is also considered. The results to multistep ahead forecasting indicated that the insertion of feedback loops increased the performances, with ARMA being the best predictor. The DE, PSO, and GA led to the minimum values of mean‐squared error during the tests, while DE yielded the smallest dispersion.

ACS Style

Hugo Siqueira; Jonatas Trabuco Belotti; Levy Boccato; Ivette Luna; Romis Attux; Christiano Lyra. Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction. International Transactions in Operational Research 2020, 1 .

AMA Style

Hugo Siqueira, Jonatas Trabuco Belotti, Levy Boccato, Ivette Luna, Romis Attux, Christiano Lyra. Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction. International Transactions in Operational Research. 2020; ():1.

Chicago/Turabian Style

Hugo Siqueira; Jonatas Trabuco Belotti; Levy Boccato; Ivette Luna; Romis Attux; Christiano Lyra. 2020. "Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction." International Transactions in Operational Research , no. : 1.

Journal article
Published: 12 September 2020 in Energies
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Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

ACS Style

Jônatas Belotti; Hugo Siqueira; Lilian Araujo; Jr. Sérgio L. Stevan; Paulo S.G. De Mattos Neto; Manoel Henrique Nóbrega Marinho; João Fausto L. De Oliveira; Fábio Usberti; Marcos De Almeida Leone Filho; Attilio Converti; Leonie Asfora Sarubbo. Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants. Energies 2020, 13, 4769 .

AMA Style

Jônatas Belotti, Hugo Siqueira, Lilian Araujo, Jr. Sérgio L. Stevan, Paulo S.G. De Mattos Neto, Manoel Henrique Nóbrega Marinho, João Fausto L. De Oliveira, Fábio Usberti, Marcos De Almeida Leone Filho, Attilio Converti, Leonie Asfora Sarubbo. Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants. Energies. 2020; 13 (18):4769.

Chicago/Turabian Style

Jônatas Belotti; Hugo Siqueira; Lilian Araujo; Jr. Sérgio L. Stevan; Paulo S.G. De Mattos Neto; Manoel Henrique Nóbrega Marinho; João Fausto L. De Oliveira; Fábio Usberti; Marcos De Almeida Leone Filho; Attilio Converti; Leonie Asfora Sarubbo. 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants." Energies 13, no. 18: 4769.

Journal article
Published: 26 March 2020 in Sustainability
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The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

ACS Style

Yslene Kachba; Daiane Maria De Genaro Chiroli; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America. Sustainability 2020, 12, 2621 .

AMA Style

Yslene Kachba, Daiane Maria De Genaro Chiroli, Jônatas T. Belotti, Thiago Antonini Alves, Yara De Souza Tadano, Hugo Siqueira. Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America. Sustainability. 2020; 12 (7):2621.

Chicago/Turabian Style

Yslene Kachba; Daiane Maria De Genaro Chiroli; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America." Sustainability 12, no. 7: 2621.

Journal article
Published: 23 October 2019 in Environmental Modelling & Software
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Estimating of daily hospital admissions due to air pollution is a leading issue in environmental science. To better understand this problem, it is essential to improve the applied methodologies. The use of Generalized Linear Models (GLM) is well known. However, they may be improved using different methods to coefficients estimation and to consider seasonality. Alternative methodologies, rarely applied in such topic, are Artificial Neural Networks (ANN), efficient to solve non-linear problems and; ensembles, which combine various models outputs. This research aims to apply 10 distinct ANN and 4 ensemble to estimate hospital admissions for respiratory diseases caused by particulate matter and meteorological variables of Campinas and São Paulo cities, Brazil. In addition, a new proposal of GLM was introduced, considering coefficients calculation via particle swarm optimization and seasonality via normalization procedure. ANN and ensembles use showed significant improvements and may allow studies into areas with flawing database, developing countries reality.

ACS Style

Lilian N. Araujo; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. Ensemble method based on Artificial Neural Networks to estimate air pollution health risks. Environmental Modelling & Software 2019, 123, 104567 .

AMA Style

Lilian N. Araujo, Jônatas T. Belotti, Thiago Antonini Alves, Yara De Souza Tadano, Hugo Siqueira. Ensemble method based on Artificial Neural Networks to estimate air pollution health risks. Environmental Modelling & Software. 2019; 123 ():104567.

Chicago/Turabian Style

Lilian N. Araujo; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. 2019. "Ensemble method based on Artificial Neural Networks to estimate air pollution health risks." Environmental Modelling & Software 123, no. : 104567.