Publication: Evaporation Forecasting through Interpretable Data Analysis Techniques
Authors
Garrido Carrera, María del Carmen ; Cadenas Figueredo, J.M. ; Bueno-Crespo, A. ; Martínez España, R. ; Giménez, J.G. ; Cecilia, J.M.
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Publisher
MDPI
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DOI
https://doi.org/10.3390/electronics11040536
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info:eu-repo/semantics/article
Description
©2022. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/
This document is the Published, version of a Published Work that appeared in final form in Electronics. To access the final edited and published work see https://doi.org/10.3390/electronics11040536
Abstract
Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved
in this process. Finally, the results of the interpretable presented model are validated with the best
literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results
very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work,
a double objective is faced: to maintain the performance obtained by the models most frequently
used in the problem while maintaining the interpretability of the knowledge captured in it, which
allows better understanding the problem and carrying out appropriate actions.
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Citation
Electronics, 11,536. 2022
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