Estimating Reference Evapotranspiration using Data Mining Prediction Models and Feature Selection

Hinessa Dantas Caminha, Ticiana Coelho da Silva, Atslands Rego da Rocha, Sílvio Carlos R. Vieira Lima

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 that models with high accuracy can be generated by M5' algorithm with feature selection techniques.

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Paper Citation


in Harvard Style

Dantas Caminha H., Coelho da Silva T., Rego da Rocha A. and R. Vieira Lima S. (2017). Estimating Reference Evapotranspiration using Data Mining Prediction Models and Feature Selection . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 272-279. DOI: 10.5220/0006327202720279


in Bibtex Style

@conference{iceis17,
author={Hinessa Dantas Caminha and Ticiana Coelho da Silva and Atslands Rego da Rocha and Sílvio Carlos R. Vieira Lima},
title={Estimating Reference Evapotranspiration using Data Mining Prediction Models and Feature Selection},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={272-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006327202720279},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Estimating Reference Evapotranspiration using Data Mining Prediction Models and Feature Selection
SN - 978-989-758-247-9
AU - Dantas Caminha H.
AU - Coelho da Silva T.
AU - Rego da Rocha A.
AU - R. Vieira Lima S.
PY - 2017
SP - 272
EP - 279
DO - 10.5220/0006327202720279