Authors:
Hinessa Dantas Caminha
1
;
Ticiana Coelho da Silva
1
;
Atslands Rego da Rocha
1
and
Sílvio Carlos R. Vieira Lima
2
Affiliations:
1
Federal University of Ceará, Brazil
;
2
INOVAGRI Institute, Brazil
Keyword(s):
Data Mining, Irrigated Agriculture, M5P, Linear Regression.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Since the irrigated agriculture is the most water-consuming sector in Brazil, it is a challenge to use water in a sustainable way. Evapotranspiration is the combination process of transferring moisture from the earth to the atmosphere by evaporation and transpiration from plants. By estimating this rate of loss, farmers can efficiently manage the crop water requirement and how much water is available. In this work, we propose prediction models, which can estimate the evapotranspiration based on climatic data collected by an automatic meteorological station. Climatic data are multidimensional, therefore by reducing the data dimensionality, then irrelevant, redundant or non-significant data can be removed from the results. In this way, we consider in the proposed solution to apply feature selection techniques before generating the prediction model. Thus, we can estimate the reference evapotranspiration according to the collected climatic variables.
The experiments results concluded tha
t models with high accuracy can be generated by M5' algorithm with feature selection techniques.
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