Comparativo de Prognósticos da Velocidade do Vento Utilizando Modelo WRF e Rede Neural Artificial

Santos, Iwldson Guilherme da Silva; Lyra, Roberto Fernando da Fonseca; Júnior, Rosiberto Salustiano da Silva

doi:10.1590/0102-77863550103

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…This ANN is characterized by the variable Z, which is the time delay. The Z factor works as a memory that provides current and previous input values (Santos, et al, 2022;Santos, et al, 2020;Samet, et al, 2019).…”

Section: Prediction Via Artificial Neural Networkmentioning

confidence: 99%

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Rede neural artificial aplicada aos casos notificados de dengue cases em Maceió – Alagoas

Santos

Júnior

Barbosa

et al. 2022

RSD

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A dengue é um dos graves problemas de saúde pública mundial. O Nordeste do Brasil (NEB) possui um clima e ambiente urbano ideal para a proliferação do mosquito Aedes (aegypti e albopictus), vetor da doença. O Estado de Alagoas, principalmente a sua capital, tem epidemias da doença de forma frequente. Portanto, o objetivo deste estudo é avaliar a aplicação de Rede Neural Artificial (RNA) nos casos notificados de dengue (CND) nas regiões administrativas (RA) de Maceió. As RAs são divididas em: RA1, RA2, RA3, RA4, RA5, RA6, RA7 e RA8. Os CND foram submetidos a RNA não linear autorregressiva (NAR) – (RNA-NAR). O período de estudo foi de 2011 a 2020. Os resultados obtidos de CND se destacaram em anos específicos (2012, 2013, 2017, 2018 e 2020), por outro lado houve superestimativas das previsões via RNA. Em algumas RAs houve subnotificações e, por isso interferiu nos resultados das previsões. A RNA-NAR foi validada, visto que a maioria das previsões apresentou correlação positiva e com resposta aos dados observados, exceto as RAs com subnotificações. O uso da RNA é adequado no alerta e previsão da donça, onde tal instrumento pode ser usado em ações preventivas de controle da doença.

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Section: Prediction Via Artificial Neural Networkmentioning

confidence: 99%

“…Table 2 shows the final configuration of the ANN-NAR training phase, the architecture was configured to provide the NDC prediction based on the input/target values in the study period. The configuration of the parameters used was based on studies Santos, et al, 2020;Santos, et al, 2022).…”

Section: Prediction Via Artificial Neural Networkmentioning

confidence: 99%

Rede neural artificial aplicada aos casos notificados de dengue cases em Maceió – Alagoas

Santos

Júnior

Barbosa

et al. 2022

RSD

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“…O Brasil tem padrões de ventos considerado um dos melhores do mundo para produção de energia eólica, pois possui condições climáticas favoráveis, velocidade do vento ao longo do ano e um fator de capacidade acima da média mundial ( [14]). A Associação Brasileira de Energia Eólica (ABEEólica), informa que, ao nal de 2020, o Brasil atingiu 17,75 GW de potência eólica instalada acumulada com quase 700 usinas eólicas instaladas e mais de 7500 aerogeradores em operação, distribuídos em 12 estados, sendo 8 da região Nordeste ([1]).…”

Section: Introductionunclassified

Previsão de Curto Prazo da Velocidade do Vento Utilizando Transformada de Wavelet Discreta e Rede Neural Artificial no Município de Craíbas-AL

Santos¹,

Souza²,

Barbosa³

et al. 2022

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. O objetivo deste estudo é analisar a previsibilidade da velocidade do vento de curto prazo, utilizando Transformada de Wavelet Discreta (TWD) e Rede Neural Articial (RNA) não linear autorregressiva com entradas externas (NARX) e sem entradas externas (NAR). Os prognósticos são aferidos com dados os observados (OBS) mensurados a cada 10 minutos, em uma torre anemométrica de 100 m de altura, localizada em Craíbas (Agreste de Alagoas) de 01/04 a 30/04 de 2015. Estes resultados, apesar de considerar um período curto de dados, indicam o potencial de aplicação da TWD e RNA na previsão da velocidade do vento.

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“…Because of these computational difficulties, there is the possibility of applying artificial intelligence (AI) techniques to facilitate the processing of WRF-Hydro, seeking an improvement of the predictions made by modeling and post-processing, where it is possible to train the network with the measured data and even history of the WRF-Hydro itself to have a faster forecast and less computational computing costs. At this point, it is important to point out that AI has already been applied in the WRF model without the coupling of the water module, as [15][16][17][18][19][20] to improve the modeled results and predictability of future data. However, the application of AI together with WRF-Hydro has still been developed, as in the studies of [21,22].…”

Section: Introductionmentioning

confidence: 99%

Feasibility Analysis on the Construction of a Web Solution for Hydrometeorological Forecasting Considering Water Body Management and Indicators for the Pandemic of SARS-COV-2

Silva

Pedruzzi

Souza

et al. 2021

Preprint

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Currently, the NCAR (U.S. National Center for Atmospheric Research), the institution responsible for the WRF-Hydro (Weather Research and Forecasting - Hydro) model initiative, highlights four major global challenges: floods, pollution, droughts, and biodiversity. Thus, given the current scenario of a global pandemic caused by the Sars-CoV-2 virus (Covid19), the importance of hydrological studies, their correlation with contamination levels, and incidence of COVID-19 cases are also in the spotlight. Among the challenges around water resources management, the lack of good and representative data, especially for small water bodies and developing countries, to perform inferences and to manage these natural resources is critical. This situation applies not only to observational data and but also to input data for hydrological models. In this context, the WRF-Hydro system represents the state of the art for hydrometeorological modeling. Thus, the model emerges as a computational tool that becomes possible to provide auxiliary data for patterns analysis in time series and computational prediction. Also, with the evolution of artificial intelligence (AI), it is possible to consider the integration of this modern approach with the WRF-Hydro model simulations. Therefore, the main of this study is to analyze the feasibility of a web tool that integrates these functionalities. The coupled WRF-Hydro with AI will support the management and generate a water predictability analysis in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), northeastern Brazil, the focus area of this study. Although the WRF-Hydro system demonstrates efficiency in the hydrometeorological simulation for the region, the model has a range of subprocesses which has a high computational cost, especially for long-term studies. Therefore, due to the possibility of integrating these computational tools, it is proposed to develop and analyze the construction of a web tool using the WRF-Hydro system for the short and medium-term with AI tools for the short term (a few hours to a few days), to optimize the computational cost. Thus, the combined application of the WRF-Hydro and AI system can improve the water bodies management and assist the identification of contamination levels by Sars-CoV-2, given the presence of the virus in water bodies and the correlation of the pandemic with hydrological variables.

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Comparativo de Prognósticos da Velocidade do Vento Utilizando Modelo WRF e Rede Neural Artificial

Cited by 4 publications

References 17 publications

Rede neural artificial aplicada aos casos notificados de dengue cases em Maceió – Alagoas

Rede neural artificial aplicada aos casos notificados de dengue cases em Maceió – Alagoas

Previsão de Curto Prazo da Velocidade do Vento Utilizando Transformada de Wavelet Discreta e Rede Neural Artificial no Município de Craíbas-AL

Feasibility Analysis on the Construction of a Web Solution for Hydrometeorological Forecasting Considering Water Body Management and Indicators for the Pandemic of SARS-COV-2

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