Abstract
ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error.
Highlights
Study and knowledge on the behavior of climatological variables are essential to understand the climate of a certain region because climatic predictions may help in the decisionmaking, aiming at either the right time for planting or the choice of the irrigation technique, and contribute to the maximization and development of agricultural production
In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology
The non-existence of a standard method to accurately predict climatic time series boosts the search for techniques that are able to perform predictions considerably close to the observed values, justifying the use of artificial neural networks (ANNs), which are Metaheuristics based on the functioning of the human brain (Braga et al, 2016)
Summary
Study and knowledge on the behavior of climatological variables are essential to understand the climate of a certain region because climatic predictions may help in the decisionmaking, aiming at either the right time for planting or the choice of the irrigation technique, and contribute to the maximization and development of agricultural production. The state of Rondônia, Brazil, and the municipality of Ariquemes are in great agricultural expansion, but studies involving climatic variables are still scarce for the region (Carvalho et al, 2016). In this context, reference evapotranspiration is an important variable for agriculture and, along with crop coefficient (Kc), is extremely important for the processes of irrigation, water use dimensioning, and production in various localities (Alves Sobrinho et al, 2011; Carvalho et al, 2011; Cavalcante Junior et al, 2011). This study aimed to model the behavior of the time series of reference evapotranspiration in the municipality of Ariquemes (RO), using a GMDH-type artificial neural network, and compare it to the SARIMA (Seasonal Autoregressive Integrated Moving Average) model
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