An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index Dérick G. F. Borges, Eluã R. Coutinho, Daniel C. P. Jorge, Marcos E. Barreto, Pablo I. P. Ramos, et al. Royal Society Open Science, 2025 Defining epidemiologically relevant placements for sentinel units is critical for establishing effective health surveillance systems. We propose a novel methodology to identify optimal sentinel unit locations using network approaches and metapopulation modelling. Disease transmission dynamics were modelled using syndromic data on respiratory diseases, integrated with road mobility data. A generalizable sentinel index is introduced as a metric that evaluates the suitability of a site to host a sentinel unit, based on topological metrics and metapopulation dynamics. A case study using syndromic data from primary health care attendances in Bahia, Brazil, validated the relevance of existing sentinel units while identifying opportunities for local re-designs to improve disease surveillance coverage.
Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho Revista Brasileira De Meteorologia, 2025 The increased consumption of natural resources, such as water, has become a global concern. Consequently, determining information that can minimize water consumption, such as evapotranspiration, is increasingly necessary. This research evaluates the capacity of Genetic Algorithms (GAs) in training and fine-tuning the parameters of Artificial Neural Networks (ANNs) (MLP-GA) to obtain daily values of reference evapotranspiration (ETo) in accordance with the Penman-Monteith FAO-56 method. The method is employed to estimate ETo at 14 weather stations in Brazil. The findings are assessed based on the coefficient of correlation (r), mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MPE), and are contrasted with the Hargreaves-Samani, Jensen-Haise, Linacre, Benavides & Lopez, and Hamon methods, along with the Multilayer Perceptron (MLP) neural network, which is conventionally trained and employs hyperparameter tuning techniques such as Grid Search (MLP-GRID) and Random Search (MLP-RD). The results show that the MLP-GA is, on average, 12 times faster than MLP-RD and 60 times faster than MLP-GRID, while achieving the highest precision indices in most regions, with an r of 0.99, MAE ranging from 0.11 mm to 0.20 mm, RMSE between 0.14 mm and 0.27 mm, and MPE between 2.49% and 7.09%. These findings suggest the results generated achieve an precision between 92.91% and 97.51% in comparison to the Penman-Monteith method. This confirms that employing Genetic Algorithms (GA) to automate the training and optimization of the model is effective and enhances the neural network's capacity to predict ETo.
Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira, Robson Mariano da Silva, Elizabeth Mendes de Oliveira, Angel Ramon Sanchez Delgado Revista Brasileira De Meteorologia, 2023 The present study applies computational intelligence techniques in the development of a hybrid model composed of Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) (MLP-GA) to estimate and fill in the gaps in the monthly variables of evaporation, maximum temperature and relative humidity to six regions in the state of Rio de Janeiro (RJ), Brazil. The results were evaluated using statistical techniques and compared with results obtained by the Multiple Linear Regression (RLM), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models and also compared with the data recorded by the weather stations. The correlation coefficient (r) between the evaporation estimates generated by MLP-GA with the recorded data showed a high relationship, remaining between 0.82 to 0.97. The average percentage error (MPE) ranged from 6.01% to 9.67%, indicating a accuracy between 90% to 94%. For the maximum temperature generated by MLP-GA the correlation with the recorded data remained between 0.97 to 0.99. It also presented the MPE between 0.95% to 1.57%, maintaining the accuracy of the estimated data between 98% to 99%. The correlation coefficient (r) between the relative humidity estimates generated with the MLP-GA remained between 0.89 a 0.97, the MPE between 1.15% to 1.89%, which guaranteed a rate higher than 98% of correctness in its estimates. Such results demonstrated gains in relation to the other applied models and allowed the accomplishment of the filling of most of the missing values.
Evaluation of methods of estimation of evapotranspiration of reference (ETO) daily for regions of the states of Rio de Janeiro and Espírito santo Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira, Robson Mariano da Silva, Elizabeth Mendes de Oliveira, Angel Ramon Sanchez Delgado Revista Brasileira De Meteorologia, 2020 Resumo O alto consumo de água pela agricultura torna cada vez mais essencial o conhecimento da evapotranspiração de referência (ETo) para a realização do manejo da irrigação. Entretanto, definir um método adequado às diferentes localidades está associado à disponibilidade dos dados meteorológicos e a adaptação dos métodos às localidades aplicadas. Assim, o objetivo deste trabalho foi comparar e avaliar o desempenho dos métodos de ETo: Hargreaves-Samani, Jensen-Haise, Benavides & Lopez e Hamon com o método padrão Penman Monteith FAO-56, para estimar a ETo diária de seis regiões, três do estado do Rio de Janeiro e três do estado do Espírito Santo. As variáveis meteorológicas empregadas foram cedidas pelo Centro de Previsão de Tempo e Estudos Climáticos e do Instituto Nacional de Pesquisas Espaciais (CPTEC - INPE). O desempenho dos modelos foi avaliado por diferentes técnicas estatísticas onde o modelo que melhor se adaptou às localidades estudadas nos dois estados foi o de Jensen Haise, tendo obtido os índices de correlação (r) entre 0,73 a 0,94 e a confiabilidade (C) entre 0,60 a 0,90 com o modelo de Penman Monteith.
Application of artificial neural networks (ANNs) in the gap filling of meteorological time series Eluã Ramos Coutinho, Robson Mariano da Silva, Jonni Guiller Ferreira Madeira, Pollyanna Rodrigues de Oliveira dos Santos Coutinho, Ronney Arismel Mancebo Boloy, et al. Revista Brasileira De Meteorologia, 2018 This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values.
