MODELOS DE MACHINE LEARNING APLICADOS NA ESTIMAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA DO PLANALTO OCIDENTAL PAULISTA

Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique.La evapotranspiración depende de la interacción entre las variables meteorológicas (radiación solar, temperatura del aire, precipitación, humedad relativa y velocidad del viento) y las condiciones fitosanitarias de los cultivos agrícolas. Es complejo construir mediciones confiables de evapotranspiración debido a los altos costos de implementar técnicas micrometeorológicas, además de las dificultades en la operación y mantenimiento de los equipos necesarios. El objetivo de esta investigación fue modelar la evapotranspiración de referencia a través de técnicas de aprendizaje automático en datos climáticos de 30 estaciones meteorológicas automáticas en el Planalto Ocidental Paulista, Estado de São Paulo, Brasil, en el período 2013-2017. Se realizó una comparación del rendimiento estadístico entre las técnicas utilizadas, donde el mejor rendimiento del modelo EToMLP4 (rRMSE = 0,62%), seguido de EToANFIS4 (rRMSE = 0,75%), EToSVM4 (rRMSE = 1,19%) y EToGRNN4 (rRMSE = 11,05 %). Las medidas de desempeño de la base de validación muestran que los modelos propuestos son capaces de estimar la evapotranspiración de referencia, con énfasis en la técnica MPL.A evapotranspiração depende da interação entre variáveis meteorológicas (radiação solar, temperatura do ar, precipitação, umidade relativa do ar e velocidade do vento) e condições fitossanitárias das culturas agrícolas. É complexo construir medidas confiáveis de evapotranspiração devido aos elevados custos para implantação de técnicas micrometeorológicas, além de dificuldades na operação e manutenção dos equipamentos necessários. O propósito desta pesquisa foi modelar a evapotranspiração de referência (ETo) por meio de técnicas de machine learning em dados climáticos de 30 estações meteorológicas automáticas do Planalto Ocidental Paulista, Estado de São Paulo, Brasil, no período de 2013-2017. Uma comparação do desempenho estatístico entre as técnicas utilizadas foi realizada onde constatou-se melhor desempenho do modelo EToMLP4 (rRMSE = 0.62%), seguido por EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) e EToGRNN4 (rRMSE = 11.05%). Medidas de performance da base de validação evidenciam que os modelos propostos são aptos à estimativa da evapotranspiração de referência com destaque para a técnica MPL. Palavras-chave: evapotranspiração; modelagem matemática; aprendizagem de máquina.   Machine learning models applied in the estimation of reference evapotranspiration from the Western Plateau of Paulista   ABSTRACT: Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique. Keywords: evapotranspiration; modeling; machine learning

Modelagem de dados de sobrevivência via modelo de risco logístico generalizado

The modeling of data of survival with the presence of covariáveis by means of the risk function has been each used time more had the easiness of interpretation One of the examples most important of risk models is the model of proportional risks considered by Cox (1972). However, this model assumes the proportionality enters the risk functions in diLerent levels of the covariáveis. To accomodate situations where the model of proportional risks is not adjusted, some types of notproportional models are being developed, as the model of sped up imperfection, considered for Prentice (1978), the model of hybrid risk of Etezadi-Amoli and Ciampi (1987) and the generalized models of hybrid risk of Louzada-Neto (1997 and 1999). In this work we explore an one new family parametric of model of dependent not-proportional risk of the time (McKenzie, 1999). This model is based on the generalization of the usual logistic function and is motivated, in part, for the necessity of if considering the eLect of the time in the modeling, and, in part, for the preference in if considering a parametric structure for the risk function. Some inferenciais procedures related this new family of models are presented.The modeling of data of survival with the presence of covariáveis by means of the risk function has been each used time more had the easiness of interpretation One of the examples most important of risk models is the model of proportional risks considered by Cox (1972). However, this model assumes the proportionality enters the risk functions in diLerent levels of the covariáveis. To accomodate situations where the model of proportional risks is not adjusted, some types of notproportional models are being developed, as the model of sped up imperfection, considered for Prentice (1978), the model of hybrid risk of Etezadi-Amoli and Ciampi (1987) and the generalized models of hybrid risk of Louzada-Neto (1997 and 1999). In this work we explore an one new family parametric of model of dependent not-proportional risk of the time (McKenzie, 1999). This model is based on the generalization of the usual logistic function and is motivated, in part, for the necessity of if considering the eLect of the time in the modeling, and, in part, for the preference in if considering a parametric structure for the risk function. Some inferenciais procedures related this new family of models are presented.Universidade Federal de Minas GeraisA modelagem de dados de sobrevivência com a presença de covariáveis por meio da função de risco tem sido cada vez mais utilizada devido a facilidade de interpretação Um dos exemplos mais importantes de modelos de risco é o modelo de riscos proporcionais proposto por Cox (1972). No entanto, este modelo supõe a proporcionalidade entre as funções de risco para duas ou mais covariáveis. Para acomodar situações em que o modelo de riscos proporcionais não é adequado, vários tipos de modelos não-proporcionais estão sendo desenvolvidos, como o modelo de falha acelerada, proposto por Prentice (1978), o modelo de risco híbrido de Etezadi-Amoli e Ciampi (1987) e os modelos de risco híbrido generalizados de Louzada-Neto (1997 e 1999). Neste trabalho exploramos um uma nova família paramétrica de modelo de risco não-proporcional dependente do tempo (McKenzie, 1999). Este modelo é baseado na generalização da função logística usual e é motivado, em parte, pela necessidade de se considerar o efeito do tempo na modelagem, e, em parte, pela preferência em se considerar uma estrutura paramétrica para a função de risco. Vários procedimentos inferenciais relacionados a esta nova família de modelos são apresentados

Sampling-based inference for the generalized time-dependent logistic hazard model

Lifetimes which satisfy a non-proportional hazard model may arise in several areas, such as, Medicine, Biometrics, Criminology and Industrial Reliability. For these data it is reasonable to presume that the hazard function is time-dependent, thereby accommodating crossing hazards. Such dependency can be modelled directly by introducing a time-dependent term in the model for the hazard function. Accordingly, in this paper we utilize a generalized time-dependent logistic (GTDL) hazard model which can accommodate non-proportional hazards data. A sampling-based inference procedure based on Markov chain Monte Carlo Methods is developed and the methodology is used to investigate survival from advanced lung cancer in a well known dataset

On the interval estimation of the parameters of a generalized time-dependent logistic model

In order to accommodating crossing hazard curves, which are non- proportional hazards, we consider in this paper a generalized time-dependent logistic hazard survival model, which has a time-dependent term. The model is a wholly parametric competitor for the Cox proportional hazard model. We compare different procedures to compute confiddence intervals for the model parameters in presence of random censoring. Our simulation study focus on the study of the coverage probabilities of these different confidence intervals and on the significance levels of some hypothesis tests. We discovered that parametric and non-parametric resampling methods can be successfully used for hypothesis testing and generating precise confidence intervals for the parameters even on small and moderate sized samples

Fuzzy inference system to study the behavior of the green consumer facing the perception of greenwashing

With increased consumption of green products, organizations have promoted their products and services as green to attract an environmentally growing segment. However, 98% of the products advertised as green have some element characterized as greenwashing, affecting consumer satisfaction. Given the need to classify subjective, ambiguous and imprecise indicators as consumer satisfaction degree of green products, a computational model of measurement is proposed that incorporates fuzzy logic techniques to reduce the incidence of uncertainty in decision analysis processes, facilitating decision-making. The fuzzy rule-based system created allows the efficient handling of uncertainties and vagueness of input data, measuring the relationships between various input variables to analyze consumer behavior and perception of greenwashing. The Mamdani's Inference Method was used to make different combinations of linguistic variables and to evaluate the relationship of these variables to consumer behavior, implementing a quantitative method of decision-making regarding the behavior of the variables. As a result, it is observed that greenwashing confuses and influences the consumer in green product confidence in retail. After the application of the system, it is concluded that the results are feasible and with the use of fuzzy logic, the system can help in the analysis and determination of the consumer satisfaction degree, and can helps companies to make future forecasts about consumer behavior of green products. The proposed approach enriches the information on the attitudes of green consumers when they perceive greenwashing. Besides, the system facilitates the decision-making and actions of both consumers and companies that apply the greenwashing as a marketing strategy242COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESNão temTo the Coordination for the Improvement of Higher Education Personnel (Capes) of Brazil, for investing in the development of this study. To the Journal, Editor, and Reviewers for the care, attention, and respect for the work done, since the conceptual aspect that we had not perceived and which enriched this paper were pointed out. Especially, thank you very much to the Editor and Reviewers for providing valuable comments and important contributions, because without his contributions we would not have been able to improve this pape