Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning

Stapleton, Adam orcid logoORCID: 0000-0003-1233-211X, Eichelmann, Elke orcid logoORCID: 0000-0001-9516-7951 and Roantree, Mark orcid logoORCID: 0000-0002-1329-2570 (2022) A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning. Applied Computing and Geosciences, 16 . ISSN 2590-1974

Abstract
A deeper understanding of the drivers of evapotranspiration and the modelling of its constituent parts (evaporation and transpiration) may be of significant importance to the monitoring and management of water resources globally over the coming decades. In this work a framework was developed to identify the best performing machine learning algorithm from a candidate set, select optimal predictive features and rank features in terms of their importance to predictive accuracy. The experiments conducted in this work used 3 separate feature sets across 4 wetland sites as input into 8 candidate machine learning algorithms, providing 96 sets of experimental configurations. Given this high number of parameters, our results show strong evidence that there is no singularly optimal machine learning algorithm or feature set across all of the wetland sites studied despite their similarities. At each of the sites at least one model was identified that improved on the predictive performance of our baseline. A key finding discovered when examining feature importance is that methane flux, a feature whose relationship with evapotranspiration is not generally examined, may contribute to further biophysical process understanding. This work demonstrates the applicability of a machine learning framework for evapotranspiration partitioning that is independent of domain knowledge, producing improved models for partitioning and identifying new and useful predictive features.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Supplementary material related to this article can be found online at https://doi.org/10.1016/j.acags.2022.100105.
Uncontrolled Keywords:Machine learning; Evapotranspiration; Optimisation; Atmospheric sciences; Geophysics; Environmental sciences
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:Elsevier
Official URL:https://dx.doi.org/10.1016/j.acags.2022.100105
Copyright Information:© 2022 The Authors.
Funders:Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (18/CRT/6183), Science Foundation Ireland Grant Number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund
ID Code:28093
Deposited On:17 Feb 2023 17:29 by Thomas Murtagh . Last Modified 14 Mar 2023 14:45
Documents

Full text available as:

[thumbnail of 1-s2.0-S2590197422000271-main.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution-Noncommercial 4.0
1MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record