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DOI: 10.14569/IJACSA.2017.080915
PDF

Estimating Evapotranspiration using Machine Learning Techniques

Author 1: Muhammad Adnan
Author 2: M. Ahsan Latif
Author 3: Abaid-ur-Rehman
Author 4: Maria Nazir

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 9, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The measurement of evapotranspiration is the most important factor in irrigation scheduling. Evapotranspiration means loss of water from the surface of plant and soil. Evaporation parameters are being used in studying water balances, water resource management, and irrigation system design and for estimating plant growth and height as well. Evapotranspiration is measured by different methods by using various parameters. Evapotranspiration varies with the climate change and as the climate has a lot of variation geographically, the pre-developed systems have not used all available meteorological data hence not robust models. In this research work, a model is developed to estimate evapotranspiration with more authentic and accurate reduced meteorological parameters using different machine learning techniques. The study reveals to learn and generalize the relationship among different parameters. The dataset with reduced dimension is modeled through time series neural network giving the regression value R=83%.

Keywords: Evapotranspiration; principle component analysis; neural network; irrigation scheduling

Muhammad Adnan, M. Ahsan Latif, Abaid-ur-Rehman and Maria Nazir, “Estimating Evapotranspiration using Machine Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 8(9), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080915

@article{Adnan2017,
title = {Estimating Evapotranspiration using Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080915},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080915},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {9},
author = {Muhammad Adnan and M. Ahsan Latif and Abaid-ur-Rehman and Maria Nazir}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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