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

3Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

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.

References Powered by Scopus

Investigating the impact of data normalization on classification performance

1053Citations
N/AReaders
Get full text

Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection

466Citations
N/AReaders
Get full text

Estimating evapotranspiration using artificial neural network

439Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

30Citations
N/AReaders
Get full text

A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks

8Citations
N/AReaders
Get full text

Crop water management using machine learning-based evapotranspiration estimation

1Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Chia, M. Y., Huang, Y. F., & Koo, C. H. (2022). ANN-Based Reference Evapotranspiration Estimation: Effects of Data Normalization and Parameters Selection. In Lecture Notes in Networks and Systems (Vol. 322, pp. 3–12). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85990-9_1

Readers' Seniority

Tooltip

Lecturer / Post doc 2

50%

PhD / Post grad / Masters / Doc 2

50%

Readers' Discipline

Tooltip

Engineering 2

67%

Agricultural and Biological Sciences 1

33%

Save time finding and organizing research with Mendeley

Sign up for free