Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing.

Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard to estimate ETo and requires several meteorological elements. In developing countries, the number of weather stations is insufficient. Thus, free products of remote sensing with evapotranspiration information must be used for this purpose. In this context, the objective of this study was to estimate monthly ETo from potential evapotranspiration (PET) made available by MOD16 product. In this study, the monthly ETo estimated by Penman-Monteith method was considered as the standard. For this, data from 265 weather station of the National Institute of Meteorology (INMET), spread all over the Brazilian territory, were acquired for the period from 2000 to 2014 (15 years). For these months, monthly PET values from MOD16 product for all Brazil were also downloaded. By using machine learning algorithms and information from WorldClim as covariates, ETo was estimated through images from the MOD16 product. To perform the modeling of ETo, eight regression algorithms were tested: multiple linear regression; random forest; cubist; partial least squares; principal components regression; adaptive forward-backward greedy; generalized boosted regression and generalized linear model by likelihood-based boosting. Data from 2000 to 2012 (13 years) were used for training and data of 2013 and 2014 (2 years) were used to test the models. The PET made available by the MOD16 product showed higher values than those of ETo for different periods and climatic regions of Brazil. However, the MOD16 product showed good correlation with ETo, indicating that it can be used in ETo estimation. All models of machine learning were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r2 (0.91), NSE (0.90) and nRMSE (8.54%) and should be preferred for ETo prediction. MOD16 product is recommended to be used to predict monthly ETo, which opens possibilities for its use in several other studies.

Increasing yoghurt daily production with modeling and simulation process / Aumento da produção diária de iogurte com modelagem e simulação de processos

ABSTRACT: The objective of this research was to use the modeling and computer simulation to support decision makers, aiming to increase the productive capacity of the agro-industry of LaticínioFunarbe. Specifically, it has modeled the current yoghurt production sector for simulation that enables it to meet the new demand. The Arena 14.7 simulation software was used to conduct the modeling. To validate the model, the output of yoghurt production collected at the factory for three months was compared with the output from the simulated computational model. Two indicators were established to perform analyzes of four different scenarios. The implemented model resulted in an increase in the production capacity of 5,000L.d-1 of yoghurt, corresponding to a production of yoghurts processed daily three times higher than the current production.

RESUMO: O objetivo deste trabalho foi usar a modelagem e a simulação computacional como ferramenta de suporte aos tomadores de decisão, visando a aumentar a produtividade da agroindústria Laticínio Funarbe. Especificamente, modelou o setor atual da produção de iogurte para elaboração de análises que possibilita atender à nova demanda. Para a modelagem utilizou-se o software de simulação Arena 14.7. Para a validação do modelo foram comparados os resultados de produção de iogurte coletados na fábrica durante três meses com os resultados simulados pelo modelo computacional. Foram estabelecidos dois indicadores para realizar análises de quatro cenários diferentes. Por meio do modelo implementado, obtivemos um aumento da capacidade produtiva de 5000L.d-1 de iogurte, que corresponde a uma produção de iogurtes processados, diariamente, três vezes maior do que a produção atual.