A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study
As a dominant terrestrial ecosystem, forests play a pivotal role, which is substantially challenged by climate extremes. At the same time, the practice of patient science to investigate and understand different intricate climate-driven phenomena is no longer an option. On the other hand, recent tech...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000238 |
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| author | Guilherme Cassales Serajis Salekin Nick Lim Dean Meason Albert Bifet Bernhard Pfahringer Eibe Frank |
| author_facet | Guilherme Cassales Serajis Salekin Nick Lim Dean Meason Albert Bifet Bernhard Pfahringer Eibe Frank |
| author_sort | Guilherme Cassales |
| collection | DOAJ |
| description | As a dominant terrestrial ecosystem, forests play a pivotal role, which is substantially challenged by climate extremes. At the same time, the practice of patient science to investigate and understand different intricate climate-driven phenomena is no longer an option. On the other hand, recent technological advancements enable scientists to simultaneously collect and analyse a large volume of complex data. High-resolution tree stem radius measurements and predictive simulation through machine learning algorithms offer powerful opportunities for understanding these dynamics. However, when these machine learning methods are applied without careful consideration of data quality, model biases, and other critical factors, their potential is often compromised. In this study, we aimed to evaluate four Deep Learning algorithms (namely CNN, LSTM, Transformer, and ETSFormer), using automatically measured and high temporal resolution tree stem radius data. We explore the complexities of handling voluminous and authentic datasets required by these algorithms. Initial experiments show that it is possible to achieve an MAE as small as 0.0026 mm on the full data. Furthermore, our study delves into the temporal resolution of data, demonstrating the feasibility of using reduced datasets without compromising accuracy levels. Our best result showed that a reduction of 97 % in collection events increases the MAE by only 6 % with the LSTM model, demonstrating that resource use optimisation can be achieved by slightly reducing the temporal resolution of data collection with marginal error increase. This also shows that LSTM can effectively capture longer-term and complex dependencies, which indicates promising future work with additional environmental data. |
| format | Article |
| id | doaj-art-4a0db7e45b1a44ef80cba641b2cd87ff |
| institution | DOAJ |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-4a0db7e45b1a44ef80cba641b2cd87ff2025-08-20T02:45:24ZengElsevierEcological Informatics1574-95412025-05-018610301410.1016/j.ecoinf.2025.103014A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case studyGuilherme Cassales0Serajis Salekin1Nick Lim2Dean Meason3Albert Bifet4Bernhard Pfahringer5Eibe Frank6TAIAO, University of Waikato, Hamilton, New Zealand; Corresponding author at: Te Ipu o Te Mahara AI Institute, Department of Computer Science – Tari Rorohiko, Private Bag 3105, Hamilton 3240, New Zealand.Scion (New Zealand Forest Research Ltd.), Rotorua 3046, New ZealandTAIAO, University of Waikato, Hamilton, New ZealandScion (New Zealand Forest Research Ltd.), Rotorua 3046, New ZealandTAIAO, University of Waikato, Hamilton, New ZealandTAIAO, University of Waikato, Hamilton, New ZealandTAIAO, University of Waikato, Hamilton, New ZealandAs a dominant terrestrial ecosystem, forests play a pivotal role, which is substantially challenged by climate extremes. At the same time, the practice of patient science to investigate and understand different intricate climate-driven phenomena is no longer an option. On the other hand, recent technological advancements enable scientists to simultaneously collect and analyse a large volume of complex data. High-resolution tree stem radius measurements and predictive simulation through machine learning algorithms offer powerful opportunities for understanding these dynamics. However, when these machine learning methods are applied without careful consideration of data quality, model biases, and other critical factors, their potential is often compromised. In this study, we aimed to evaluate four Deep Learning algorithms (namely CNN, LSTM, Transformer, and ETSFormer), using automatically measured and high temporal resolution tree stem radius data. We explore the complexities of handling voluminous and authentic datasets required by these algorithms. Initial experiments show that it is possible to achieve an MAE as small as 0.0026 mm on the full data. Furthermore, our study delves into the temporal resolution of data, demonstrating the feasibility of using reduced datasets without compromising accuracy levels. Our best result showed that a reduction of 97 % in collection events increases the MAE by only 6 % with the LSTM model, demonstrating that resource use optimisation can be achieved by slightly reducing the temporal resolution of data collection with marginal error increase. This also shows that LSTM can effectively capture longer-term and complex dependencies, which indicates promising future work with additional environmental data.http://www.sciencedirect.com/science/article/pii/S1574954125000238Point dendrometerTree stem radiusTime series forecastingDeep learningArtificial intelligenceCNN |
| spellingShingle | Guilherme Cassales Serajis Salekin Nick Lim Dean Meason Albert Bifet Bernhard Pfahringer Eibe Frank A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study Ecological Informatics Point dendrometer Tree stem radius Time series forecasting Deep learning Artificial intelligence CNN |
| title | A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study |
| title_full | A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study |
| title_fullStr | A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study |
| title_full_unstemmed | A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study |
| title_short | A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study |
| title_sort | comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer a case study |
| topic | Point dendrometer Tree stem radius Time series forecasting Deep learning Artificial intelligence CNN |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000238 |
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