Comparison of machine learning methods emulating process driven crop models
Article
Article Title | Comparison of machine learning methods emulating process driven crop models |
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ERA Journal ID | 4673 |
Article Category | Article |
Authors | Johnston, David B., Pembleton, Keith G., Huth, Neil I. and Deo, Ravinesh C. |
Journal Title | Environmental Modelling and Software |
Journal Citation | 162 |
Article Number | 105634 |
Number of Pages | 12 |
Year | Apr 2023 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 1364-8152 |
1873-6726 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.envsoft.2023.105634 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1364815223000208 |
Abstract | Performing large scale simulation analyses using complex process-driven models can be very time consuming and incur significant computational expense. These analyses involve generating synthetic datasets and include processes such as impacts analysis (IA) and variance-based sensitivity analysis (SA). Machine learning (ML) provides a potential alternative path to reduce computational costs incurred when generating output from large simulation experiments. We assessed the accuracy and computational efficiency of three ML-based emulators (MLEs): artificial neural networks, multivariate adaptive regression splines, and random forest algorithms, to replicate the outputs of the APSIM-NextGen chickpea crop model. The MLEs were trained to predict seven outputs of the process-driven model. All the MLEs performed well (R2 > 0.95) for predicting outputs for the training data set locations but did not perform well for previously unseen test locations. These findings indicate that modellers using process-driven models can benefit from using MLEs for efficient data generation, provided suitable training data is provided. |
Keywords | Metamodels; Surrogates |
ANZSRC Field of Research 2020 | 300205. Agricultural production systems simulation |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Agriculture and Environmental Science |
Centre for Sustainable Agricultural Systems | |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/y9yy5/comparison-of-machine-learning-methods-emulating-process-driven-crop-models
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