E3S Web Conf.
Volume 347, 20222nd International Conference on Civil and Environmental Engineering (ICCEE 2022)
|Number of page(s)||8|
|Section||Water and Environmental Engineering|
|Published online||14 April 2022|
Feasibility of bootstrap aggregating to enhance extreme learning machine for reference evapotranspiration estimation
Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti
Tunku Abdul Rahman, Malaysia.
* Corresponding author: firstname.lastname@example.org
Estimation of evapotranspiration (ET) is a challenging, yet important task as the ET value can be used to predict many other natural phenomena. In this work, the reference evapotranspiration (ET0) was estimated using the extreme learning machine (ELM) at two meteorological stations located in the northern region of the Straits of Malacca. Optimum designs of the ELM were first determined and it was found that the different number of hidden neurons and activation functions were favourable to various input combinations. In order to enhance the performance of the ELM, the bootstrap aggregating algorithm was integrated to resample the original dataset. However, the performance of bagged-ELM was found to be poorer than the base ELM. This could be attributed to the high stability of the base ELM model whereby the training size already overwhelmed the dimensionality of the problem itself. The bootstrap aggregation data fusion technique produced a “backfire” effect that degraded the accuracy and generalisability of the base ELM model.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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