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ANN-Based Reference Evapotranspiration Estimation: Effects of Data Normalization and Parameters Selection

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Proceedings of International Conference on Emerging Technologies and Intelligent Systems (ICETIS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 322))

Abstract

Accurate prediction of evapotranspiration (ET) is important in regions where agricultural plantations such as oil palm are abundant. This study was conducted in Peninsular Malaysia which has a large coverage of oil palm plantations that depend on rain-fed irrigation. This study attempts to improvise the estimation of reference evapotranspiration (ET0) to aid formulation of irrigation strategies. In order to obtain desirable estimations, data pre-treatment such as normalization and input selections are essential crucial steps that are needed. Therefore, it is an aim of this study to present the effect of normalization techniques on three specific ANN-based models for estimating ET0; namely the multilayer perceptron (MLP), radial basis function (RBF) and generalized regression neural network (GRNN). The case of different combinations of climatic parameters as input would be considered. Among the ANN models, the GRNN had the highest stability that could produce relatively stable performance regardless of the input combinations. Incorporation of normalization techniques prior to the training of the ANN-based models enabled diluting the effect of reduced input climatic parameters. For the MLP, the effect of normalization was minimal and insignificant. Selection of normalization technique of the RBF model should take the spread value of the model into consideration.

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Acknowledgements

This research was funded by Universiti Tunku Abdul Rahman (UTAR), Malaysia through the Universiti Tunku Abdul Rahman Research Fund under project number IPSR/RMC/UTARRF/2018-C2/K03.

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Authors and Affiliations

  1. Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia

    Min Yan Chia, Yuk Feng Huang & Chai Hoon Koo

Authors
  1. Min Yan Chia
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  2. Yuk Feng Huang
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  3. Chai Hoon Koo
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Corresponding author

Correspondence to Yuk Feng Huang .

Editor information

Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  2. Universiti Teknologi Malaysia, Johor, Malaysia

    Mohammed A. Al-Sharafi

  3. Al Buraimi University College, Al Buraimi, Oman

    Mohammed N. Al-Kabi

  4. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

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Chia, M.Y., Huang, Y.F., Koo, C.H. (2022). ANN-Based Reference Evapotranspiration Estimation: Effects of Data Normalization and Parameters Selection. In: Al-Emran, M., Al-Sharafi, M.A., Al-Kabi, M.N., Shaalan, K. (eds) Proceedings of International Conference on Emerging Technologies and Intelligent Systems. ICETIS 2021. Lecture Notes in Networks and Systems, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-85990-9_1

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