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Mexican International Conference on Artificial Intelligence

MICAI 2018: Advances in Soft Computing pp 315–326Cite as

A Rainfall Prediction Tool for Sustainable Agriculture Using Random Forest

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11288))

Abstract

In recent years world’s governments have focused its efforts on the development of the Sustainable Agriculture were all resources, especially water resources, are used in a more environmentally friendly manner. In this paper, we present an approach for estimating daily accumulated rainfall using multi-spatial scale multi-source data based on Machine Learning algorithms for three HABs in the Andean Region of Colombia where the agricultural activities are one of the main production activities. The proposed approach uses data from different rain-related variables such as vegetation index, elevation data, rain rate and temperature with the aim of the development of a rain forecast, able to respond to local or large-scale rain events. The results show that the trained model can detect local rain events event when no meteorological station data was used.

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Acknowledgments

The authors are grateful to the Telematics Engineering Group (GIT) and the Optics and Laser Group (GOL) of the University of Cauca, The University of Cauca, Meteoblue, RICCLISA Program, and the AgroCloud project for supporting this research, as well as the AQUARISC program for the PhD support granted to Cristian Valencia-Payan.

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Authors and Affiliations

  1. Universidad del Cauca, Popayán, Colombia

    Cristian Valencia-Payan & Juan Carlos Corrales

Authors
  1. Cristian Valencia-Payan
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  2. Juan Carlos Corrales
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Corresponding author

Correspondence to Cristian Valencia-Payan .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Ildar Batyrshin

  2. Universidad Panamericana, Mexico City, Mexico

    María de Lourdes Martínez-Villaseñor

  3. Faculty of Engineering, Universidad Panamericana, Mexico City, Mexico

    Hiram Eredín Ponce Espinosa

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Valencia-Payan, C., Corrales, J.C. (2018). A Rainfall Prediction Tool for Sustainable Agriculture Using Random Forest. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-04491-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04490-9

  • Online ISBN: 978-3-030-04491-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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